Clinical decision model

ABSTRACT

An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of the healing rate of an acute traumatic wound is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of the healing rate of an acute traumatic wound.

This application is a continuation of PCT Application No.PCT/US2009/060850, filed on Oct. 15, 2009, which claimed the benefit ofU.S. patent application Ser. No. 61/105,786 filed Oct. 15, 2008 and U.S.patent application Ser. No. 61/166,245 filed Apr. 2, 2009, which arehereby incorporated by reference. The International Application No.PCT/US2009/060850 was published on Apr. 22, 2010.

I. FIELD OF THE INVENTION

The present invention relates to a model for providing apatient-specific diagnosis of disease using clinical data. Moreparticularly, the present invention relates to a fully unsupervised,machine-learned, cross-validated, and dynamic Bayesian Belief Networkmodel that utilizes clinical parameters for determining apatient-specific probability of the healing rate of an acute traumaticwound.

II. BACKGROUND OF THE INVENTION

Within this application several publications are referenced by arabicnumerals within brackets. Full citations for these, and other,publications may be found at the end of the specification immediatelypreceding the claims. The disclosures of all these publications in theirentireties are hereby expressly incorporated by reference into thepresent application for the purposes of indicating the background of thepresent invention and illustrating the state of the art.

Thyroid nodules represent a common problem brought to medical attention.Four to seven percent of the United States adult population (10-18million) has palpable thyroid nodules, and up to 50% of American womenolder than age 50 have nodules visible by thyroid ultrasound [1]. Themajority (>95%) of thyroid nodules are benign; however, malignancy riskincreases with male gender, nodule size, rapid growth and associatedsymptoms, extremes of age (<30 and >60 years), underlying autoimmunedisease (e.g. Graves' Disease), nodule growth under thyroid hormonesuppression, personal or family history of thyroid malignancy, andradiation exposure [2].

Thorough history and physical examination, serum thyrotropin (TSH)level, thyroid ultrasound, and fine need aspiration biopsy (FNAB) areutilized to evaluate patients with thyroid nodules. Patients withthyroid nodules and normal or elevated serum TSH typically undergothyroid ultrasound to determine if FNAB is warranted. Nodules with amaximum diameter greater than 1.0-1.5 cm with solid elements, or nodulesof any size demonstrating suspicious features on ultrasound shouldundergo FNAB [3]. Given the increased risk of malignancy in thyroidincidentalomas detected by 18FDG-PET (Fluorodeoxyglucose orFludeoxyglucose positron emission tomography) (14-50%) or sestamibi scan(22-66%), FNAB is indicated under these circumstances [4, 5].Functioning thyroid nodules (suppressed TSH, hyperfunctioning onradionuclide scan) do not require FNAB in the absence of clinicallysuspicious findings.

Fine needle aspiration biopsy is a cost effective and accuratediagnostic tool for thyroid nodules. In experienced hands, thesensitivity and specificity of FNAB are very high, 95% and 99%,respectively, in positive and negative cases [6]. A six tieredclassification system for FNAB is favored that is associated withincreased risk of malignancy across the spectrum of: unsatisfactory ornon-diagnostic specimen (unknown), benign (<1%), follicular lesion(atypia) of undetermined significance (5-10%), follicular neoplasm(20-30%), suspicious for malignancy (50-75%), and malignant (100%) [3].Over 20% of patients undergoing FNAB of a thyroid nodule haveindeterminate cytology (follicular neoplasm), and they require and areexposed to the function-limiting complications (impaired voice andswallowing) of thyroid lobectomy/isthmusectomy conducted purely for thepurpose of attaining a more definitive diagnosis. Given that themajority of patients with follicular neoplasms have benign surgicalpathology, thyroidectomy in these patients is conducted principally withdiagnostic intent [7]. Electrical impedance scanning (EIS) is anothertool for scanning thyroid nodules [9, 10]. Utilization of EIS can resultin a significant reduction (67%) in the number of purely diagnosticthyroid resections for follicular neoplasms [8, 9].

Fine needle aspiration cytology has a high diagnostic accuracy and is apracticable test for the initial evaluation of thyroid nodules. However,the efficacy of FNA for the differential diagnosis of follicular andHurthle cell neoplasms remains imperfect. As the majority of detectedthyroid nodules are benign and cytology, even in the best of hands, isindeterminate in 20% of fine needle aspirates, the frequency ofdiagnostic or non-therapeutic thyroid resection is increasing.

As the majority of patients with indeterminate FNA cytology have benignnodules, surgical operations are undertaken primarily with diagnosticintent. Thus, it is difficult to non-invasively differentiate benign andclinically inconsequential low-risk malignant nodules from those thatindeed stand to benefit from resection. Color Doppler sonography withquantitative analysis of tumor vascularity, in conjunction withconventional ultrasonographic assessment of echogenicity, halo,microcalcifications, and tumor size, may provide a means fordifferentiating malignant from benign solid thyroid nodules in thepre-operative setting [11-14]. However, the predictive value of thiscombined technique is achieved by compromising diagnostic sensitivity[15]. The predictive value of ultrasonography may be enhancedsignificantly through the application of ultrasound thyroid elastography[16-17]. The application of 18F-FDG PET shows high sensitivity for thediagnosis of malignancy in thyroid nodules demonstrating indeterminatecytology on pre-operative FNA. However, the low specificity of thetechnique limits its utility [18-19].

Cellular changes alter the flow of electrical current through livingtissue, and differences in cellular electrical signature betweenmalignant and non-malignant tissue has been identified and studiedextensively since the 1920′s [20]. EIS devices measure tissue impedancecharacteristics and identify irregularities in conductance andcapacitance that are associated with increased levels of cellularactivity and malignant transformation [21]. EIS measurements areobtained by introducing a known, low-level, biocompatible, alternatingcurrent to the body via a hand-held electrical signal generator. Thesignal is directed through the measured tissue and collected via anon-invasive surface probe. EIS is safe, feasible, and diagnosticallyaccurate in detecting differences in the bioelectrical signature ofbenign and malignant tissue through body surface measurements ofsuspicious skin lesions and lymph nodes, and breast abnormalities[22-30]. EIS is a safe, rapid, realtime, and non-invasive imagingmodality with a predictive value sufficient to make it an adjunct toFNA, particularly in the setting of indeterminate cytology [8, 9].

Recognizing that individual variables, though independently associatedwith thyroid cancer, are insufficient in predicting the risk ofmalignancy in any given thyroid nodule, multivariate predictivealgorithms have been developed to determine the cumulative risk ofmalignancy for this clinical problem [10, 31]. One predictive algorithmutilizes a multivariate stepwise regression model to predict malignancyin thyroid nodules in a highly selected patient population on the basisof patient age, calcifications in a sonographically solid nodule, andFNAB cytology [10]. Another predictive algorithm applies multivariatemodeling in patients with indeterminate thyroid nodules to define malegender, nodule size exceeding 4 cm, and character of the gland bypalpation (dominant nodule in multi-nodular goiter) to predict the riskof thyroid malignancy [31]. The development of this predictive algorithmwas limited to a narrow population of patients with follicular neoplasiaby FNAB, and did not include imaging-based variables according tostandard of practice in the predictive model.

Many electronic clinical decision support systems have been developedthat rely on human expertise to develop decision-support rules ratherthan calculating a specific estimate of outcome using historical sourcedata. Such “expert systems” take two forms. The first form is a systemwhere clinical experts, following a systematic review of the literature,devise a system of static decision making rules for clinical decisionsupport. The second form is a system where clinicians in the treatingfacility, usually basing their judgments on personal experience and theliterature, devise a set of rules for clinical decision making in theirown institution. The rules developed under both systems can either beimplemented in publication format, in the form of published guidelines,or as a set of static decision rules in a clinical informatics system.

Transplant glomerulopathy (TG) is another disease that is difficult todiagnose. Transplant glomerulopathy is a distinctive lesion identifiedhistologically on allograft biopsy and is associated with rapid declinein glomerular filtration rate and poor outcome. It is defined by acharacteristic doubling of the glomerular basement membrane as well asincreasing evidence that supports an immunologic pathogenesis; however,the molecular pathways involved have not been elucidated. Currently,transplant glomerulopathy must be diagnosed by microscopy, whether lightor electron, at a minimum and thus necessitates an advanced diseasestage, for which there is no cure.

Long-term kidney allograft function continues to improve modestly,despite dramatic improvements in acute rejection rates and short termpatient and graft survivals. Measurement of serum creatinine istypically the primary monitoring modality following kidneytransplantation. Significant changes in serum creatinine, and/or thedevelopment of proteinuria, result in a series of maneuvers to definethe many potential etiologies of acute and chronic allograftdysfunction. Allograft biopsy is the current standard of thesemaneuvers, although morphologic analysis may not easily distinguishthese etiologies. Furthermore, the analysis may be limited in regards toprognostic importance and functional outcome.

Gene expression analysis using microarrays and real-time polymerasechain reaction (PCR) has been applied broadly in the field of renaltransplantation. Gene expression changes found in renal biopsies, urinesediment, and peripheral white blood cells have been used to evaluateallografts with stable function, acute rejection, and chronic allograftdysfunction. In addition, gene expression within the renal allograftpre-reperfusion or reperfusion periods has been correlated with delayedgraft function and medium term allograft survival.

Several well-established relationships support that such an approach toidentifying TG has biologic relevance. The relationship betweenpathology and cell signaling (chemokine expression), cell trafficking(adhesion molecule expression) and tissue remodeling (MMP expression) issupported by current models of TG. TG is believed to be secondary tobinding of donor specific antibodies to endothelium with resultingstimulation and recruiting of secondary mediators leading to aninflammatory response. This inflammatory response and subsequent tissueinjury has been associated with chemokine, adhesion molecule and MMPexpression. Additionally, adhesion molecule expression has been shown tobe associated with both chronic disease and stable function in renaltransplant recipients. Alteration of chemokine expression has beenlinked to costimulatatory molecules (CD28, 40L, 80, 86) and IL-10 hasbeen demonstrated to be elevated in allografts with stable function. Thedevelopment of TG and Cd4 expression has also been well characterized.

The majority of modern war wounds are caused by blasts and high-energyballistics [32-34]. Complex traumatic wounds require aggressive surgicalcare, including serial debridements to remove devitalized tissue anddecrease bacterial load. Positive-pressure irrigation, negative-pressureand vacuum-assisted closure (VAC) have improved wound management[35-36]. However, despite these technological advances, the basicsurgical decision regarding appropriate timing of surgical traumaticwound closure or coverage remains very subjective.

Poorly defined pathophysiology of acute wound failure partiallycontributes to the difficulties of objectively assessing wound healing.Current criteria for wound closure or coverage consider many subjectivefactors, which include the patient's general condition, injury location,adequacy of perfusion, and the gross appearance of the wound. Factorsused to assess the patient's general condition include nutritional andnonspecific systemic inflammatory parameters. Relevance of injurylocation and visual assessment of the wound, such as the appearance ofgranulation tissue, are subjectively determined by the surgeon. Thus,there is considerable intra-observer variability in wound assessment.Furthermore, the decision making process used to make wound closuredetermination are ill-defined. After evaluating these factors, surgeonsoften reach a wound status determination base on his/her experience anddiscretion. Therefore, even in the hands of seasoned surgeons, somewounds ultimately fail. Unfortunately, other wounds with the biologicability to heal will undergo unnecessary surgical debridements, addingtreatment costs and exposing patients to additional anesthetic andsurgical morbidity risk. Objective criteria and decision algorithms todefine the appropriate timing of wound closure are needed.

The molecular landscape of the wound ultimately determines the fate ofthe wound healing process. Acute wounds typically heal by aninterdependent sequence of events mediated by inflammatory messengers.The wound healing process generally has three phases. They are theinflammatory phase, the proliferative phase, and the maturational phase(or remodeling phase). The inflammatory phase is characterized byhemostasis and inflammation and typically lasts one to three days. Afterinjury to tissue occurs, damaged cell membranes immediately releasethromboxane A2 and prostaglandin 2-alpha, potent vasoconstrictors. Thisinitial response helps to limit hemorrhage. After a short period,capillary vasodilatation occur secondary to local histamine release, andthe cells responsible for inflammation are able to migrate to the woundbed. The timeline for cell migration in a normal wound healing processis predictable.

Platelets, the first response cell, release multiple chemokines,including epidermal growth factor (EGF), fibronectin, fibrinogen,histamine, platelet-derived growth factor (PDGF), serotonin, and vonWillebrand factor. These factors help stabilize the wound through clotformation. They act to control bleeding and limit the extent of injury.Platelet degranulation also activates the complement cascade,specifically C5a, which is a potent chemoattractant for neutrophils.

As the inflammatory phase continues, more immune response cells migrateto the wound. Neutrophil, the second response cell, is responsible fordebris scavenging, complement-mediated opsonization of bacteria, andbacteria destruction via oxidative burst mechanisms (superoxide andhydrogen peroxide formation). The neutrophils kill bacteria anddecontaminate the wound from foreign debris.

The next cells present in the wound are the leukocytes and themacrophages (monocytes). Macrophage is essential for wound healing.Numerous enzymes and cytokines are secreted by the macrophage, includingcollagenases, which debride the wound; interleukins and tumor necrosisfactor (TNF), which stimulate fibroblasts (production of collagen) andpromote angiogenesis; and transforming growth factor (TGF), whichstimulates keratinocytes. This marks the transition into the process oftissue reconstruction, the proliferative phase.

Epithelialization, angiogenesis, granulation tissue formation, andcollagen deposition are the principal steps in the proliferative phaseof wound healing. Epithelialization occurs early in wound repair. If thebasement membrane remains intact, the epithelial cells migrate upwardsin the normal pattern, as in first-degree skin burn. The epithelialprogenitor cells remain intact below the wound, and the normal layers ofepidermis are restored in 2-3 days. If the basement membrane has beendestroyed, similar to a second- or third-degree burn, then the wound isreepithelialized from the normal cells in the periphery and from theskin appendages, if intact (eg, hair follicles, sweat glands).

Angiogenesis, stimulated by TNF-alpha, is marked by endothelial cellmigration and capillary formation. The new capillaries deliver nutrientsto the wound and help maintain the granulation tissue bed. The migrationof capillaries into the wound bed is critical for proper wound healing.The granulation phase and tissue deposition require nutrients suppliedby the capillaries, and failure for this to occur results in achronically unhealed wound. Mechanisms for modifying angiogenesis areunder study and have significant potential to improve the healingprocess.

The final part of the proliferative phase is granulation tissueformation. Fibroblasts differentiate and produce ground substance andthen collagen. The ground substance is deposited into the wound bed.Collagen is then deposited as the wound undergoes the final phase ofrepair. Many different cytokines are involved in the proliferative phaseof wound repair. The steps and the exact mechanism of control have notbeen elucidated. Some of the cytokines include PDGF, insulin like growthfactor (IGF), and EGF. All are necessary for collagen formation.

The final phase of wound healing is the maturational phase. The woundundergoes contraction, ultimately resulting in a smaller amount ofapparent scar tissue. The entire wound healing process is a dynamiccontinuum with an overlap of each phase and continued remodeling. Woundreaches maximal strength at one year and result in a tensile strengththat is 30% of normal skin. Collagen deposition continues for aprolonged period, but the net increase in collagen deposition plateausafter 21 days.

Proper wound healing involves a complex interaction of cells andcytokines working in concert. Particularly, cytokines and chemokinesorchestrate the progression of healing and are fundamental to thecellular and biochemical events that occur during acute wound healing.These effectors can be measured in serum and wound effluent using modernmolecular techniques.

Currently, the only available commercial product proven to beefficacious in wound healing is PDGF, which is available as recombinanthuman PDGF-BB. In multiple studies, recombinant human PDGF-BB has beendemonstrated to reduce healing time and improve the incidence ofcomplete wound healing in stage III and IV ulcers. Other cytokines beingstudied for wound healing include TGF-beta, EGF, and IGF-1.

Breast carcinoma is the most commonly diagnosed cancer and the secondleading cause of cancer-related mortality among women in the UnitedStates [50]. In 2009, there were over 192,000 estimated new cases ofcancer of the breast, and over 40,000 disease-specific deaths [50].Breast cancer-related mortality rates have steadily decreased over thepast two decades, largely due to improved disease detection and therapy[51].

As breast cancer in younger (under age 40) women is infrequentlydiagnosed in the early stages utilizing current screening guidelines,improved cancer screening and detection methods are important in currentresearch, particularly in younger, at-risk women [52]. Breast cancer inyounger women typically has unfavorable prognostic characteristicsassociated with increased disease-specific mortality [53-55]. Youngerwomen are not typically referred for periodic imaging unless they areidentified as being “high risk” [56]. “At risk” younger women withsignificant family history or genetic factors are encouraged to undergofrequent clinical and annual breast imaging surveillance, and toconsider chemoprevention.

While increased surveillance for “at risk” women may be beneficial, thevalue of this approach is restricted by the rarity of breast cancer dueto known genetic risk factors [57, 58]. Over 90% of breast cancers aredetected in women who are not identified as “high risk” [52].Furthermore, screening mammography is generally less accurate in youngerwomen and those with increased breast tissue density commonlyencountered in women under age 40 [59]. The reduced sensitivity ofmammography for dense breasts impacts age groups in which a “life saved”often results in “higher” personal and societal costs in terms ofaltered life expectancy and personal productivity [60].

MRI is being used increasingly as a screening modality in high-riskwomen with a significant family history of breast cancer, or BRCA1 orBRCA2 gene mutations resulting in lifetime risk of cancer exceeding 20%[61]. Hence, breast MRI is applied to a relatively small proportion ofall women. MRI is unaffected by breast tissue density; however, the highcost, requirement for intravenous contrast administration, and variablespecificity limit its feasibility for widespread population-basedscreening [62, 63].

Tamoxifen is considered in both pre- and post-menopausal women, andRaloxifene is considered in post-menopausal women, with lobularcarcinoma in situ (LCIS) or with a 5-year breast cancer risk estimate of≥1.66% (according to the Gail Model or the NCI Breast Cancer RiskAssessment Tool), in order to reduce the risk of estrogenreceptor-positive (ER+) breast cancer [64]. In the NSABP P-1 study,Tamoxifen (20 mg/day for 5 years) consistently reduced the incidence ofbreast cancer by 49% in at-risk women across all study age and riskgroups (women age 35-59 with a ≥1.66% risk, those ≥60, or with priorLCIS), thereby demonstrating the efficacy of chemoprevention for thisdisease [65]. The MORE, CORE, RUTH and NSABP STAR Trials demonstratedconsistent significant reductions in ER+ breast cancer incidence inat-risk post-menopausal women [64]. Subsequent analyses of the NSABP P-1study data suggested improved quality-adjusted survival and costeffectiveness when Tamoxifen was initiated as early as age 35 in at-risk(Gail Model 5-year risk ≥1.66%) women [66, 67].

Lifetime relative risk assessment tools (e.g., Gail model) are availableto identify women over age 35 years who are at-risk for breast cancer.However, the predictive value of mathematical models to estimate breastcancer risk varies according to age, menopausal status, race/ethnicity,and family history of breast cancer. Instruments such as the Gail modelare imperfect for identifying increased cancer risk in younger women[68]. Current risk prediction models estimate population, not individuallevels of breast cancer risk. Currently, the only criterion generallyused to identify high-risk young women who could benefit fromchemoprevention is family/genetic history. The value of this riskestimation paradigm is limited by the rarity of breast cancer due toknown gene mutations.

III. SUMMARY OF THE INVENTION

An embodiment of the invention provides a highly predictive clinicaldecision support tool to assist physicians in determining personalizedrisk of disease (malignancy, transplant glomerulopathy, healing rate ofan acute traumatic wound, and/or breast cancer risk). For instance, inat least one embodiment, a Bayesian Belief Network model is trainedusing a machine learning algorithm applied to the specific patient studypopulation with thyroid nodules characterized by relevant clinicalvariables. The algorithm is used to develop a model-derived riskassessment tool that supports clinical decision making on the basis ofindividual patient risk of malignancy rather than traditional risk-poolallocation. An integrated predictive decision model using Bayesianinference, which incorporates readily obtainable thyroid nodule measures(e.g., size, sonographic and impedance characteristics, and aspirationcytology), effectively predicts cancer in patients presenting withthyroid nodules. A broad statistically validated network structure ofmultiple clinical variables provides a universal method to individualizepatient care. This predictive risk assessment tool refines clinicaldecision making using multiple available parameters as well as partialinformation by providing case-specific risk scores in an operationallycomputational manner. The risk assessment tool and predictive model isupdated continuously to include new clinical, treatment, and outcomeinformation in order to expand its decision support capability. Thedynamic, quantitative case-specific predictions made by the predictivemodel allow the clinical decision support tool to be adapted to thespecific needs and capabilities of a given medical clinic. Given thefollowing enabling description of the drawings, the apparatus shouldbecome evident to a person of ordinary skill in the art.

IV. BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is described with reference to the accompanyingdrawings. In the drawings, like reference numbers indicate identical orfunctionally similar elements.

FIG. 1 is a flow diagram illustrating a method for pre-operativelydetermining a patient-specific probability of disease according to anembodiment of the invention;

FIG. 2 illustrates a process of model development and deploymentaccording to an embodiment of the invention;

FIG. 3 illustrates a process for implementing the system according to anembodiment of the invention;

FIG. 4 illustrates a receiver-operating characteristic curve accordingto an embodiment of the invention;

FIG. 5 illustrates a table of the cross-validation results according toan embodiment of the invention;

FIG. 6 illustrates the structure of the BBN network model according toan embodiment of the invention;

FIGS. 7A-7F illustrate BBN network models for predicting thyroidmalignancy according to embodiments of the invention;

FIG. 8 illustrates an inference table according to an embodiment of theinvention;

FIG. 9 illustrates a system for pre-operatively determining apatient-specific probability of malignancy in a thyroid nodule accordingto an embodiment of the invention;

FIGS. 10A-C illustrate a BBN network model of relative-fold expressionchanges in a panel of genes according to an embodiment of the invention;

FIGS. 11A-C illustrate a BBN network model emphasizing surrogatebiomarkers according to an embodiment of the invention;

FIGS. 12A-B illustrate a BBN network model for determining theprobability of transplant glomerulopathy using Banff C4d depositionaccording to an embodiment of the invention;

FIG. 13 is a table illustrating stable function versus transplantglomerulopathy using the Laminin and Matrix Metalloproteinase-7 genesaccording to an embodiment of the invention;

FIG. 14 is a table illustrating gene symbols of current wound healingtarget genes according to an embodiment of the invention;

FIG. 15A-15J is a table illustrating gene names according to anembodiment of the invention;

FIG. 16 is a table illustrating patient (wound) demographics accordingto an embodiment of the invention;

FIG. 17 illustrates mean APACHE II scores at each wound debrigement(P<0.05) according to an embodiment of the invention;

FIG. 18 illustrates serum cytokine and chemokine at each wounddebridement of patient with Normal (dark grey) and Impaired (light grey)Wound Healing according to an embodiment of the invention (all data isdepicted on a logarithmic scale as mean ± SEM. P<0.05; MFI is the meanfluorescent intensity, which correlates to analyte concentration);

FIG. 19 illustrates selected VAC effluent cytokine and chemokine atdifferent debridement according to an embodiment of the invention(IL-12p(4), RANTES, and IL-5 each show a significant difference inconcentration at time of closure between wounds that healed and woundsthat dehisced);

FIG. 20A illustrates wound biopsy gene expression of dehisced woundsrelative to healed wounds at initial debridement according to anembodiment of the invention (the y-axes are fold-expression of thetarget gene in the wounds that dehisced relative to the wounds thathealed);

FIG. 20B illustrates wound biopsy gene expression of dehisced woundsrelative to healed wounds at final debridement according to anembodiment of the invention;

FIG. 21A illustrates a wound prediction model according to an embodimentof the invention;

FIG. 21B illustrates the receiver-operating characteristics of the crossvalidation according to an embodiment of the invention;

FIG. 21C-21G illustrate wound prediction models according to embodimentsof the invention;

FIG. 22 is a table illustrating characteristics of the study populationby age according to an embodiment of the invention;

FIG. 23 is a table illustrating characteristics of the study populationby biopsy according to an embodiment of the invention;

FIG. 24 illustrates a BBN-ML for predicting breast cancer risk accordingto an embodiment of the invention;

FIG. 25 is a table illustrating cross validation statistics according toan embodiment of the invention;

FIGS. 26A-C illustrate a BBN-ML for predicting breast cancer riskaccording to an embodiment of the invention;

FIG. 27 illustrates an inference table showing the probability of biopsydiagnosis given the Gail model risk estimate and breast EIS resultaccording to an embodiment of the invention; and

FIG. 28 illustrates a program storage device according to an embodimentof the invention.

V. DETAILED DESCRIPTION OF THE DRAWINGS

FIG. 1 provides a general overview of one method for pre-operativelydetermining a patient-specific probability of disease (e.g., malignancyin a thyroid nodule, transplant glomerulopathy, wound healing, andbreast cancer) according to an embodiment of the invention. Details ofthis and other embodiments of the invention are described below withreference to FIGS. 2-22. As described more fully below, a fullyunsupervised machine-learned Bayesian Belief Network model (referred toherein as the “BBN-ML”) is created, updated, and deployed withouthuman-developed decision support rules. A machine learning algorithmallows the BBN-ML to learn dynamically from data that resides in a datawarehouse. The machine learning algorithm automatically detects andpromotes significant relationships between variables without the needfor human interaction. This allows for the processing of vast amounts ofcomplex data quickly and easily into a tractable Bayesian network. Thestructure of the network provides the user with immediate knowledgeabout the nature of the problem set and the relative significance ofvariables to the outcome of interest. By inputting current knowledgeinto the BBN-ML, the user obtains a probability of outcome and relativerisk in real-time.

The method collects clinical parameters from patients to create atraining database (110). As described more fully below, examples of theclinical parameters include a plurality of age, ethnicity, functionalstatus of a thyroid nodule, pre-operative assessment, number of cervicallymph nodes, lymph node size, serum thyrotropin level, fine needleaspiration biopsy results, ultrasound data, nuclear medicine rating,imaging data, and/or biomarkers from serum and/or biopsy tissue.Although in some embodiments, not all of the example clinical parametersare use in a particular BBN-ML

A fully unsupervised Bayesian Belief Network model is created using datafrom the training database (120); and, the BBN-ML is validated (130). Inat least one embodiment, the structure of the BBN-ML is a directedacyclic graph that is learned natively from prior probabilities residentin the training database. Each node in the directed acyclic graphrepresents a clinical parameter and includes two or more bins. Each binrepresents a value range for the clinical parameter (e.g., bin 1: geneexpression level less than or equal to 1.0; bin 2: gene expression levelgreater than 1.0). As described below, a node can be created such thateach bin in the node includes an equal number of data points. Forexample, the value ranges of bins 1-3 can be created such that 33% ofthe training population is in each bin. In at least one embodiment,cross-validation is performed, wherein the data is randomized intogroups of matched training and test data, a classifier is trained oneach of the training sets created in the data preparation step using thesame data discretization and modeling parameters. Then eachcorresponding test set is used to create a set of case-specificpredictions. A Receiver-Operating Characteristic (ROC) curve is plottedfor each test exercise to estimate model robustness and classificationaccuracy. Upon completion, the best model structure is documented in,for example, XML format for deployment as the BBN-ML. In at least oneembodiment, the relevant learning parameter and modeling log files arestored if future audits are performed.

The method in at least one embodiment collects the clinical parametersfrom an individual patient (140), which are received into the BBN-ML(150). The patient-specific probability of disease is output from theBBN-ML to a graphical user interface for use by a clinician inpre-operative planning (160). As described more fully below, theBayesian models are in an interactive format such that a clinician canselect an outcome or relative gene expression level by clicking on thegraphical user interface and observing corresponding changes to theprobability distribution of the remaining variables. The graphical userinterface is also used to enter current, patient-specific data andreceive an evidence-based prediction of diagnosis (e.g., transplantglomerulopathy or stable function), thus enabling patient-riskstratification and clinical intervention.

The method updates the BBN-ML using the clinical parameters from theindividual patient and the patient-specific probability of disease(170). As illustrated in FIG. 2, according to at least one embodiment,the ongoing process of model development and deployment 200 is one ofdata collection 210, model development 220, model validation 230, modeldeployment (i.e., diagnosis) 240, and iteration 250. This process is notstatic; it includes constant update, validation, and improvement. As newdata is collected, models are updated and QC/QA documented.

FIG. 3 illustrates a process flow for implementing a system forpredicting a patient-specific probability of disease according to anembodiment of the invention. A clinician 310 runs diagnostic test(s) ona patient; and, results are written to a patient database 320 (alsoreferred to herein as a “data warehouse” or “training database”). Thedatabase 320 sends, for example, an XML message with raw patientdiagnostic data to a batch inference application programming interface(API) 330. The batch inference API 330 communicates with a model 340(also referred to herein as the “BBN-ML”) and receives apatient-specific prediction. The batch inference API 330 sends XMLmessages with the patient-specific prediction to the patient database320 and a graphical user interface 350.

A. Malignant Thyroid Nodules

The following description provides examples of using the systems andmethodologies of the embodiments of the invention for diagnosingmalignant thyroid nodules. It is recognized, however, that theembodiments of the invention could be utilized to diagnose other organsand/or diseases, such as, for example transplant glomerulopathy andtransplant outcome, wound healing, and breast cancer. One method usesavailable clinical information (e.g., patient age, ethnicity, thyroidfunctional status, surgeon pre-operative assessment, and/or number ofcervical lymph nodes), available diagnostic information (e.g., fineneedle aspiration biopsy results, nodule characterization by ultrasound,nodule size by ultrasound, and/or nuclear medicine rating), imagingtechnology (e.g., EIS), and a computer classification model (i.e., theBBN-ML).

The clinical and diagnostic (e.g., cytology and imaging) data iscollected in the patient's electronic medical record. The EIS data iscollected through an examination of the patient by the clinician. Thecombined data is used to train the BBN-ML, which is validated in atleast one embodiment using cross-validation. The validated BBN-ML isused to calculate a probability that the pathology of an individualthyroid nodule is malignant or benign. The BBN-ML provides a probabilitydistribution for the pathology of the nodule, along with an estimatedaccuracy, which includes sensitivity, specificity, positive and negativepredictive value, and overall accuracy. The estimate can be calculatedusing at least two methods. The first method assembles the relevant dataparameters into a tabular file which is read by software using an API,which returns posterior probabilities for outcomes of interest using allevidence in the tabular file. The second method employs a graphical userinterface for a physician to enter data manually.

A vexing and common clinical problem is definitively diagnosing thecytologically indeterminate thyroid nodule. This often necessitatesdiagnostic operation in a large proportion of patients with so-called“follicular neoplasms” to possibly benefit the few patients with actualthyroid malignancy. Accurately predicting malignancy in any giventhyroid nodule is a clinical challenge. An embodiment of the inventionprovides decision support tools and predictive models that are based onclinical, image-based, as well as cytological predictors of malignancy,to guide therapeutic decision making. The sensitivity and specificityderived using the BBN-ML provides a level of confidence and accuracy.The BBN-ML reduces the amount of unnecessary surgical operation with itsattendant healthcare system costs and surgical morbidity impairing voiceand swallowing function and quality of life. The BBN-ML is a prognosticrisk assessment tool constructed and in at least one embodimentcross-validated, which provides an individual patient-specificprediction of cancer in thyroid nodules.

An embodiment of the invention utilizes a BBN-ML that compares knownpatient characteristics to known outcomes in the training population toarrive at a highly individualized, patient-specific estimate of risk ofthyroid malignancy, rather than using generalized rules to assign thepatient into a risk/survival pool. Although at least one embodiment ofthe invention creates the BBN-ML utilizing data collected in a clinicalstudy of EIS on thyroid nodules before thyroidectomy [8, 9], it isrecognized that training data may be obtained from other sources. ABayesian classifier was trained on a prospectively enrolled cohortcollected over a four year period (Sept. 2002-Dec. 2006) in the contextof a previously published institutional review board (IRB) approvedclinical trial including thyroid impedance, ultrasound imaging,cytological and histopathological outcome data [8, 9]. Thus, aprospective single arm observational cohort trial was used evaluatingthe diagnostic accuracy of pre-operative thyroid EIS in patientsscheduled to undergo thyroidectomy.

In at least one embodiment of the invention, thyroid EIS was conductedprior to thyroid surgery using a T-Scan 2000ED (available from TransScanMedical (Mirabel®, Anchorage, Ak.)). Baseline conductivity andcapacitance measurements of the sternocleidomastoid muscle wereobtained. The thyroid gland was scanned in a high-resolution targetedmode with a flat multi-array thyroid probe held by the clinician whilethe patient held a metal cylinder. The source of a low-level andbiocompatible (e.g., alternating current) electrical signal (e.g., 1.0to 2.5 volts, maximum current 5 mA) was transmitted from the cylinder,up the arm, and across the neck. Impedance recordings of conductivityand capacitance were obtained over the entire thyroid gland in apredetermined sequence using a real-time image acquisition techniqueover a broad frequency range (e.g., frequency range, 50-20,000 Hz). Agray-scale impedance map was obtained that provides an anatomical imagecorresponding to the area of interest directed to a palpable orsonographic thyroid nodule.

Homogeneous gray scale impedance maps (uniform conductivity andcapacitance) are characteristic of normal or benign thyroid nodules,which demonstrate similar conductivity and capacitance (or impedance) tonormal thyroid tissue. A focal disturbance in the electrical fielddistribution can be seen by a malignant tumor due to its increasedconductivity and/or capacitance (or decreased impedance), which appearsas a focal bright white spot on the gray scale impedance map. Skinsurface lesions and/or artifacts (e.g., incomplete probe contact,insufficient conducting gel, clavicular head) can appear as white spotsas well. In order to arrive at an appropriate malignancy riskdetermination, the lesions and artifacts are taken into account andcorrelated with specific localization of the thyroid nodule(s). Changesfrom baseline sternocleidomastoid conductivity and capacitance arecalculated for the thyroid nodule(s). A positive EIS examination isdefined as a focal bright spot over a thyroid nodule, correlating withincreased conductivity (decreased impedance) and/or capacitance greaterthan 25% of the baseline sternocleidomastoid muscle impedance, absentconfounding local artifacts [8, 9].

An embodiment of the invention includes reviewing impedance scansconducted in the previous trial [8, 9], blinded to fine needle aspiratecytology and surgical pathology results. An EIS level of suspicion (LOS)score was developed on the basis of a focal white spot presence andincreased conductivity and/or capacitance (with the previouslyestablished cutoff) associated with the palpable or sonographic thyroidabnormality. Thyroid nodule level of suspicion was classified asfollows: LOS 1: definitely benign; LOS 2: highly unlikely to bemalignant; LOS 3: unlikely to be malignant; LOS 4: likely to bemalignant; and, LOS 5: highly likely to be malignant. Thyroid nodulescorresponding to a palpable or sonographic abnormality determined tohave LOS of 4 or 5 was considered EIS-positive; otherwise the noduleswere regarded as EIS-negative.

In one embodiment of the invention, the study subjects underwent thyroidresection after thyroid US, FNA, and EIS. Surgical histopathology wascorrelated with sonographic, cytological, and impedance findings andinterpretations. FNAB and surgical specimens were evaluated byexperienced board-certified cytologists and thyroid pathologists whorendered a cytological and histopathological diagnosis without knowledgeof EIS level or suspicion for malignancy. In at least one embodiment,the study data was collected and curated into a data set consisting of218 subjects. Biopsy results were classified based on establishedclinical guidelines into either benign or malignant diagnoses andassembled into a master data set. The master data set was randomizedinto additional (e.g., 10) cross-validation sets. Each subject recordwas assigned a randomly generated number. These numbers were used toassign the subjects to a number of test groups (e.g., 10). A matchedtraining set was created for each test group, which excluded the testgroup.

In at least one embodiment, once the data set was prepared, the data formodeling was prepared using a discretization engine consisting ofalgorithms for curating the data prior to modeling. Discrete factors(e.g., binaries, text or qualitative data) were prepared using discretebinning, wherein each state in the field corresponded to a state in thedistribution. Continuous factors (e.g., cost and length of stay) wereprepared using a granular equal-probability-density binning approachthat provided for stratification while acknowledging and controlling forthe non-normal distributions observed in a clinical population. Thus,within the allowable range of a given feature with a continuousdistribution, this method ensured that the prior probabilitydistributions for each feature used for training the classifier had anequal population in each numeric range.

The predictive models are built by applying a set of heuristics togenerate predictive models with different conditional independenceassumptions. The conditional independence assumptions are represented asa directed acyclic graph, wherein the structure of the networkrepresents a hierarchy of conditional independence which allows the userto identify the best estimators of a given outcome and also to identifyother features that can act as proxies when critical prior probabilitiesare missing. The BBN-MLs encode the joint probability distributions ofall of the variables in the clinical data set from the previous clinicaltrial by building a network of conditional probabilities [8, 9]. TheBBN-MLs provide a network incorporating parent-child relationshipsbetween nodes. The network is queried to provide estimates for posteriorprobabilities given a priori knowledge, and tested for accuracy usingdata withheld from the training model. The predictive models areconstructed using a machine learning algorithm (e.g., FasterAnalytics™(available from DecisionQ, Washington, D.C.)) that supports a MinimumDescription Length (MDL) scoring metric for network optimization. MDLscoring ensures that the final model represents the most likely modelgiven the data used for learning and the model variations underconsideration.

The machine learning algorithm allows the computer to learn dynamicallyfrom the data that resides in the data warehouse. The machine learningalgorithm automatically detects and promotes significant relationshipsbetween variables without the need for human interaction using a scoringalgorithm to optimize the network for robustness. This allows for theprocessing of vast amounts of complex data quickly and easily into atractable Bayesian network. The structure of the network provides theuser with immediate knowledge about the nature of the problem set andthe relative significance of variables to the outcome of interest. Byinputting current knowledge into the BBN-ML, the user obtains aprobability of outcome and relative risk in real-time. Further, agraphical representation of the network is provided to the user. Thisprovides the user with the likely rationale for the outcome of interestand knowledge about additional information used to confirm or refute thepredicted outcome. An embodiment of the invention utilizes a step-wisemodeling process to optimize the accuracy of the BBN-ML. The developmentof the BBN-ML is an iterative process consisting of several steps, theend product of which is a predictive model that supports subsequentdynamic re-training with new data. The process streamlines variableselection and preparation to produce the optimum outcome.

In at least one embodiment of the invention, model creation begins withpreliminary modeling to identify appropriate machine learning parametersand data quality issues. A base level of association in the dataset andobvious associations that are interfering with the base level(confounding features) are also identified. Feature analogs (i.e.,features that are proxies for one another and reduce accuracy) areidentified and removed by the operator. Next, the operator uses thepruned features to train a new classifier in order to assess and setappropriate machine learning parameters. Appropriate changes are made tothe data set, including the removal of analogs and confounding featuresand further binning The model is explored relative to the literature anddomain expertise as a “check” and to analyze relationships. Linear naïvemodeling is also performed on dependent outcomes of interest to identifythe relative contribution of features. A quantitative contributionreport is also prepared in at least one embodiment.

Following the pruning and qualitative validation process, final focusedmodeling is performed, wherein heuristic search is performed using onlysubsets of variables identified in prior steps discussed in the previousparagraph. A network is obtained that is more focused than the networkproduced in the prior steps. By excluding certain variables, theremaining variables are explored more exhaustively. The focused model isexplored and preliminary reports are automatically created. In at leastone embodiment, manual modeling is also performed to enhance the focusedmodel. Specifically, the structure of relationships is changed manuallyusing a user interface to incorporate expert information beyond what thedata contains.

Cross-validation is performed, wherein classifier is trained on each ofthe training sets created in the data preparation step using the samedata discretization and modeling parameters. Each corresponding test setis used to create a set of case-specific predictions. Moreover, a ROCcurve is plotted for each test exercise to calculate classificationaccuracy. In at least one embodiment, a 10% holdback dataset is withheldfrom the initial dataset to be used for prospective validation. Uponcompletion of cross-validation, the best model is documented in XMLformat for deployment as the BBN-ML. The relevant learning parameter andmodeling log files are stored if audits are performed in the future. Allcross-validation files are also stored for future audits; and, a reportsummarizing the results is prepared.

In at least one embodiment, the network is validated using atrain-and-test ten-fold cross-validation methodology. Ten-foldcross-validation is performed, wherein ten unique sets of data are usedthat have been randomized in 90% train/10% test pairs. This producesclassification accuracy estimates across ten exercises and calculatesthe classification error. Predictive values are calculated byclassifying the outcome (i.e., surgical pathology diagnosis) for a giveninstance and comparing this prediction to the known value in anindependent test set. The test set predictions are used to calculate aROC curve and confusion matrix by threshold for each test set by theclinical feature of interest. The ROC curve is calculated by comparingthe predicted value for each feature of interest to the known value inthe test set on a case-specific basis. The ROC curve is used tocalculate the area-under-the-curve (AUC), a metric of overall modelquality, and positive predictive value (PPV), a measure of theprobability that a positive is a true positive given a specifiedprobability threshold for the variable of interest. The BBN analysisdetermines if a clinically relevant BBN-ML-derived prognostic riskassessment tool can be constructed and cross-validated.

According to one embodiment of the invention, FIG. 4 illustrates the ROCcurve and predictive values for the BBN-ML as tested a posteriori withthe master data set. The completed BBN-ML is cross-validated using thetrain and test sets and also tested a posteriori against the master dataset to assess predictive power. FIG. 5 illustrates a table of thecross-validation results for each train-and-test pair and the aposteriori testing results according to an embodiment of the invention.Because the internal a posteriori testing results are very similar tothe cross-validation results and the cross-validation results areinternally consistent, it is accurate to describe this model as highlyrobust and highly predictive and it is appropriate to use the internalROC curves. The model operates at an 80% negative predictive value (NPV)and an 83% PPV, as well as an 88% AUC for both outcomes (benign andmalignant thyroid nodules).

FIG. 6 illustrates the structure of a BBN-ML for predicting an overallpathology diagnosis (DX) 600 (i.e., benign or malignant thyroid)according to an embodiment of the invention. Predictors (also referredto herein as “clinical parameters”) of the BBN-ML include pre-operativediagnosis 610A, worst FNA 610B, ultrasound nature overall 610C, maximumnodule ultrasound size 610D, nuclear medicine overall 610E, thyroidstatus 610F, age 610G, worst EIS 610H, cervical lymph nodes 6101, andethnicity 610J. Because the BBN-ML provides an estimate of theprobability diagnosis 600, whether a result is classified as benign ormalignant is a function of the probability threshold used to determinewhether a result is considered a positive or negative. Thus, thepredictive values (NPV and PPV) are a function of the threshold used andcan be optimized for the relative cost of Type I and Type II errors. Inthe cross-validation process described above, it was determined that a50% threshold (most likely case) produced optimum results.

In one embodiment of the invention, the BBN-ML structure defines fourpredictors of thyroid nodule histopathology: FNA cytology (i.e., worstFNA 610B), maximum nodule size (i.e., maximum nodule ultrasound size610D), EIS characteristics (i.e., worst EIS 610H), and ultrasoundcharacteristics of the nodule (i.e., ultrasound nature overall 610C).The relative contribution of each of these four factors is determined byexcluding each factor one at a time in a posteriori analysis against themaster data set. When the worst EIS 610H predictor is eliminated fromthe BBN-ML, the AUC for benign and cancer is 84.2% and 84.6%,respectively, and the PV value (at 50%) for benign and cancer is 75.8%and 71.3%, respectively. When the worst FNA 610B predictor is eliminatedfrom the BBN-ML, the AUC for benign and cancer is 88.0% and 83.0%,respectively, and the PV value (at 50%) for benign and cancer is 79.5%and 82.9%, respectively. Eliminating the ultrasound nature overall 610Cpredictor from the BBN-ML, yields a AUC for benign and cancer of 88.2%and 88.4%, respectively, and a PV value (at 50%) for benign and cancerof 80.4% and 81.6%, respectively. When the maximum nodule ultrasoundsize 610D predictor is eliminated from the BBN-ML, the AUC for benignand cancer is 92.0% and 91.9%, respectively, and the PV value (at 50%)for benign and cancer is 83.5% and 81.6%, respectively. The onlypredictor that significantly degraded the BBN-ML's predictive power wheneliminated from the network is the worst EIS 610H predictor. Eliminationof the maximum nodule ultrasound size 610D predictor improved thepredictive power of the BBN-ML; thus, this predictor is included in theBBN-ML.

According to one embodiment of the invention, model structure isinterpreted based on these results. First order predictors of thyroidpathology are FNA cytology 610B, ultrasound characteristics 610C,maximum nodule size 610D, and EIS characteristics 610H of the thyroidnodule. Diagnosis 600 is conditionally dependent with FNA cytology 610B,as FNA is incorporated in pre-operative diagnosis. Furthermore, patientage, thyroid nodule size, scintigraphic findings (hot, warm, cold), andEIS characteristics are clustered together in a conditional dependencenetwork. The conditional dependence between these factors accounts forthe potential overlap of predictors and prevents over-fitting of theBBN-ML to the data. This supports the overall robustness of the BBN-ML.In at least one embodiment, as the training database is updated and newknowledge is interpreted, the structure of the BBN-ML (i.e., dependencyrelationships between the nodes) is revised by the heuristic search andscoring algorithms. FIG. 7A illustrates the expected referenceprobability distributions for each variable (i.e., 610A-610J) in thetraining population according to an embodiment of the invention. Eachreference distribution represents the expected distribution of a givenmodel feature in a normal pre-operative population similar to the studypopulation utilized herein, prior to the addition of knowledge about anindividual case.

With a trained, tested, and cross-validated BBN-ML, case-specificpredictions are made by adding prior knowledge about a given case, suchas, for example an EIS result (i.e., worst EIS 610H) or ultrasoundcharacterization of a thyroid nodule (i.e., US nature overall 610C).FIG. 7B illustrates a posterior estimate of surgical pathology outcomeaccording to an embodiment of the invention. The final estimatedpathology diagnosis 600 for a given case with thyroid nodule EIS levelof suspicion of 2 (worst EIS 610H=highly unlikely to be malignant) has aposterior probability of cancer of 19%.

As illustrated in FIG. 7C, adding a thyroid nodule ultrasound finding610C of solid to the EIS level of suspicion 610H of 2 refines thecase-specific posterior estimate of malignancy 600 to 23%, which is lessthan the cancer rate in the study population (54%). Additional datarefines the prediction of malignancy 600 even further, such as anindeterminate FNA cytology 610B. As illustrated in FIG. 7D, a solidnodule by ultrasound 610C having an EIS level of suspicion 610H of 2 hasa posterior probability of benignity 600 of 85% (15% probability ofmalignancy). Further, as illustrated in FIG. 7E, changing the EIS result610H from probably highly unlikely to be malignant to level of suspicionof 4 (likely to be malignant) increases the posterior probability ofmalignancy 600 from 15% to 65%.

As described above, the conditional dependency of the predictors610A-610J, and thus the configuration of the nodes in the BBN-ML canchange depending upon the data in the training database. FIG. 7Fillustrates a BBN-ML for predicting malignancy in a thyroid noduleaccording to another embodiment of the invention, wherein US natureoverall 610C, worst EIS 610H, and worst FNA 610B are the first orderpredictors of thyroid pathology. Worst FNA 610B is conditionallydependent upon pre-op Dx classed 610A and maximum nodule US size 610D.Nuclear medicine overall 610E can be used to predict worst EIS 610H;and, age 610G can be used to predict nuclear medicine overall 610E andmaximum nodule US size 610D. Moreover, maximum nodule US size 610D andnuclear medicine overall 610 E are conditionally dependent upon oneanother.

Inference-based individual case-specific estimates of posteriorprobability from the Bayesian Belief Network can be attained by applyingthe BBN-ML to new data sets in either batch inference mode or bytabulating all potential combinations in an inference table. FIG. 8illustrates an inference table calculated using the BBN-ML for allpotential combinations of EIS 610H and FNA 610B results according to anembodiment of the invention. For example, in row 6 of the table, aDefinitely Benign EIS (Level of Suspicion of 1) and an Indeterminate FNAcytology has a frequency estimate of 4.7% in the population and aprobability of cancer of 5.7%. However, in row 9 of the table, a patientwith an indeterminate nodule with an EIS Level of Suspicion of 4 (likelyto be malignant) has a 13.6% estimated frequency in the trainingpopulation and a 58.7% probability of thyroid malignancy.

FIG. 9 illustrates a system 900 for pre-operatively determining apatient-specific probability of malignancy in a thyroid nodule accordingto an embodiment of the invention. A patient database 910 includesindividual patient records. This can either be a standalone database oran existing clinical information system, such as an electronic healthrecord database. The system 900 uses patient data to train newiterations of the model 940 (also referred to herein as the “BBN-ML”)and to make individual patient predictions.

Machine learning software 920 is also used to retrain the model 940 withnew data. The machine learning software 920 includes a configurationsfile 930, which contains the settings for learning. The model 940 is anXML model that specifies structure and joint probability distributions.The batch inference API 950 uses the model 940 and individual patientdata from the patient database 910 to produce patient-specificpredictions. A graphical user interface (GUI) 960 (e.g., web-based orclient-server) receives the patient-specific predictions in the form ofreports.

B. Transplant Glomerulopathy

Another embodiment of the invention inputs real-time PCR data into theBBN-ML to predict biomarker expression in renal allograft biopsyspecimens. The biomarker expression is highly predictive of renaltransplant glomerulopathy versus stable allograph function. The BBN-MLanalyzes large datasets of diverse data types to identify associationsbetween clinical, genomic, and proteomic variables. Use of the BBN-ML isextendable to define surrogate marker profiles of specific pathologieswithin renal allografts using biological products of gene expressionfound in blood, serum, urine, and/or tissue biopsies as surrogateendpoints.

At least one embodiment of the invention utilizes gene expression datafrom allograph biopsies to create a network of Bayesian models. The mostrobust model is documented in XML format for deployment as the BBN-ML.As described below, the Bayesian models are in an interactive formatsuch that a clinician can select an outcome or relative gene expressionlevel by clicking on the graphical user interface and observingcorresponding changes to the probability distribution of the remainingvariables. The graphical user interface is also used to enter current,patient-specific data and receive an evidence-based prediction ofdiagnosis (e.g., transplant glomerulopathy or stable function), thusenabling patient-risk stratification and clinical intervention.

An embodiment of the invention built the network of Bayesian modelsbased on a retrospective review of 963 renal transplant core biopsiesfrom 166 patients. It is recognized, however, that other studies couldbe utilized to build the network of Bayesian models. The review of 166patients identified transplant glomerulopathy in 20 biopsies from 18patients using (10.8%) Banff classification. The mean grade (± SD) oftransplant glomerulopathy was 2.65 ± 0.49. A cohort of patients (n=32)with stable function allografts were studied for comparison. Thebiopsies were analyzed for transcript expression of 87 genes, across twogene panels, with quantitative real-time PCR. An embodiment of theinvention derives relative transcript quantification using quantitative,real-time PCR and the 2^(−Ct) method [70]. This calculation wasperformed with normalization to 18S ribosomal RNA expression as aninternal control per sample and is relative to pooled cDNA from livedonors undergoing open donor nephrectomy. Data was analyzed (e.g., usingthe machine learning algorithm) and dependence relationships betweentranscript expression and transplant glomerulopathy was established. Aprobabilistic Bayesian model was generated for each gene panel andvalidated to predict histopathology based on gene expression signatures(e.g., FIGS. 10A-C and FIG. 11A-C). Ten non-overlapping sets of tenpercent of the biopsies were excluded randomly from the initial dataset(i.e., 90% train, 10% test) and subsequently used for a ten-foldcross-validation of model robustness. In the validation analysis, theGene Panel 1 Bayesian model effectively identified stable functionversus transplant glomerulopathy with an overall sensitivity of 87.5%and a specificity of 85.7%. The Gene Panel 1 also yielded a PPV of 77.8%and a NPV of 92.3%. The Gene Panel 2 model was just as robust with 84.4%sensitivity and 80.0% specificity. The Gene Panel 2 also yielded a PPVof 87.1% and a NPV of 76.2%.

FIGS. 10A-C illustrate Bayesian models of relative-fold expressionchanges in a panel of genes related to immune function (i.e., Model 18b,Gene Panel 1) according to an embodiment of the invention. The GenePanel 1, in this example, includes the following genes: ICAM1, IL10,CCL3, CD86, CCL2, CXCL11, CD80, GNLY, PRF1, CD40LG, IFNG, CD28, CXCL10,CCR5, CD40, CTLA4, TNF, CXCL9, CX3CR1, FOXP3, EDN1, CD4, TBX21, FASLG,C3, CD3E, CXCR3, and CCL5 (i.e., variables 1020A-1020AB, respectively).In the Bayesian model illustrated in FIG. 10A, diagnosis 1010 is dividedinto stable function (SF) or transplant glomerulopathy (TG). Probabilitydistributions are shown immediately left of the bar graph. For instance,the diagnosis 1010 has a 39.38% probability of SF and a 60.62%probability of TG. Variables (also referred to herein as “clinicalparameters” or “predictors”) 1020A-1020M are divided into threeequal-area bins of fold expression relative to pooled normal kidneyexpression levels. These ranges are derived by normalizing the availablerange of information into units contained equal densities ofobservations. For instance, for the variable 1020A, 32.21% of thepatients in the training database have a ICAM1 gene expression levelless than 1.04 relative fold expression; 32.12% of the patients in thetraining database have a ICAM1 gene expression level between 1.04 and1.84 relative fold expression; and 35.67% of the patients in thetraining database have a ICAM1 gene expression level greater than 1.84relative fold expression. The dotted box 1000 indicates a subset ofgenes (i.e., 1020A-10201) for the focus illustrated in FIGS. 10B and10C. In at least one embodiment, the ranges are derived by normalizingthe available range of information into unit ranges such that each rangecontains equal numbers of observations.

A diagnosis 1010 of SF is associated with a down regulation of geneexpression or only small increases in gene expression. For instance, asillustrated in FIG. 10B, the upper binned expression levels forvariables 1020A-1020I (i.e., genes ICAM1, IL10, CCL3, CD86, CCL2,CXCL11, CD80, GNLY, and PRF1)are: >1.04=9.09%; >22.9=69.09%; >3.15=9.01%; >8.89=17.48%; >1.19=23.13%; >106=23.14%; >20.6=26.97%; >18.6=29.6%;and >22.9=31.63%, respectively. On the other hand, a diagnosis 1010 ofTG is associated with increased expression of several genes within thebiopsies. For instance, as illustrated in FIG. 10C, the upper binnedexpression levels for variables 1020A-1020Iare: >1.04=52.94%; >22.9=52.94%; >3.15=52.7%; >8.89=47.2%; >1.19=43.52%; >106=48.53%; >20.6=41.06%; >18.6=39.38%;and >22.9=39.12%, respectively.

In regards to the variable 1600A discussed above with reference to FIG.10A, changing the diagnosis 1010 to 100% stable function automaticallyadjusts the expected expression levels for the ICAM1 gene. Thus, for apatient with stable function, the Bayesian model estimates a 72.73%probability that the patient would have an ICAM1 gene expression levelless than 1.04 relative fold expression. Moreover, the Bayesian modelestimates an 18.18% probability that the patient would have an ICAM1gene expression level less between 1.04 and 1.84 relative foldexpression and a 9.09% probability of an ICAM1 gene expression levelgreater than 1.84 relative fold expression.

FIGS. 11A-C illustrate Bayesian models emphasizing surrogate biomarkersover a current C4d grade in predicting transplant glomerulopathy (Model19b, Gene Panel 2) according to an embodiment of the invention. The GenePanel 2 in this example includes the following genes: VCAM1, MMP9, BanffC4d, MMP7, LAMC2, TNC, S100A4, NPHS1, NPHS2, AFAP, PDGF8, SERPINH1,TIMP4, TIMP3, VIM, SERPINE1, TIMP1, FN1, ANGPT2, TGFB1, ACTA2, TIMP2,COL4A2, MMP2, COL1A1, COL3A1, GREM1_2, SPARC, IGF1, SMAD3, HSPG2, FN1,ANGPT2, TGFB1, ACTA2, THBS1, CTNNB1, FGF2, TJP1, FAT, CDH1, SMAD7,CD2AP, CDH3, CTGF, ACTN4, SPP1, AGRN, VEGF, and BMP7 (variables1120A-1120AT, respectively). The dotted box 1100 indicates a subset ofvariables (i.e., variables 1120A-1120E) for the focus of the Bayesianmodels illustrated in FIGS. 11B and 11C.

Banff C4d 1120C is an immunohistochemical stain used in transplantation.It is associated with transplant glomerulopathy. The probability of caseis the probability of that scenario occurring in the dataset. Modelgrade of C4d staining (Banff C4d 1120C), along with expression patternsof genes related to endothelial activation and fibrosis, yielded aBayesian model that accurately related a higher grade with transplantglomerulopathy (FIG. 11A; diagnosis 1110=TG probability of 81.25%).However, this Bayesian model further illustrates that transplantglomerulopathy biopsies (100% for 3.0 fold expression) have a fairlyeven distribution of C4d grades, thereby suggesting a high rate of falsenegatives for this variable alone (FIG. 11B; diagnosis 1110=TGprobability of 100.00%). Elevated expression of the MMP7 1120D and LAMC21120E genes is more clearly related to a diagnosis 1110 of transplantglomerulopathy. Setting MMP7 1120CD and LAMC2 1120E with knowledge oftheir coincident expression levels indicates a 96.64% probability oftransplant glomerulopathy (FIG. 11C).

FIGS. 12A-C illustrate Bayesian models for determining the probabilityof transplant glomerulopathy (i.e., diagnosis 1210) using relative-foldexpression of the ICAM1, IL10, CCL3 and CD86 genes (variables1220A-1220D, respectively) (Model 18b, Gene Panel 1) according to anembodiment of the invention. Given known, relative expression levels ofgenes, both directly and indirectly related to the diagnosis 1210, theBayesian models provide a biopsy-specific outcome estimate. Coincidentupregulation of the ICAM1, IL10, and CCL3 genes (variables 1220A-1220C,respectively) is indicative of transplant glomerulopathy (FIG. 12A;diagnosis 1210=TG probability of 99.67%). Additionally, upregulation ofthe CD86 gene (variable 1220D) of 8.89-fold or greater estimates theprobability of transplant glomerulopathy as 81.61% (FIG. 12B). Theinteractive Bayesian models quickly bring to light biological pathwaysas illustrated by the adjusted probability distributions throughout themodels when known evidence is set.

A clinician interested in describing the biological pathways closelyassociated with stable function versus those associated with transplantglomerulopathy utilizes the Bayesian models to elucidate potentialtargets by setting evidence of diagnosis to either option, which is doneby clicking on the graphs (i.e., adjusting the cross-hatched bars in thediagnosis box 1210). The respective changes in posterior probabilitydistributions (changes in cross-hatched bars in the gene boxes, relativeto the cross-hatched bars FIG. 10A and FIG. 11A) instantaneously reportsthe coordinated dependence of each variable on the specific diagnosis.In other words, the clinician can adjust the diagnosis to either TG orSF by clicking on the cross-hatched bars in the diagnosis box 1210. Thisautomatically adjusts the cross-hatched bars in the gene boxes,respective to the new diagnosis.

Conversely, setting known relative expression levels of genesimmediately yields a posterior estimate of diagnosis. In other words,adjusting the relative expression level of a gene by clicking on across-hatched bar in one of the gene boxes automatically adjusts thediagnosis in diagnosis box 1210. For instance, FIG. 12A illustrates thatthe current dataset (by up regulation of ICAM1, IL10, and CCL3 geneexpression relative to pooled normal renal biopsies) yields a diagnosis1210 of 99.67% likelihood of transplant glomerulopathy. When the upperexpression level of the CD86 gene is adjusted from 72.74 (FIG. 12A) to100.00 (FIG. 12B), the diagnosis 1210 is automatically adjusted to an80.61% likelihood of transplant glomerulopathy.

A subset (43/52) of the biopsies in the study utilized herein has beenstained for peritubular C4d deposition, which was identified in 15/18(83.3%) with transplant glomerulopathy and 8/25 (32.0%) with stablefunction (p<0.001). A Banff C4d grade of 3 is strongly associated withthe presence of transplant glomerulopathy (81% probability in FIG. 11A).Additional genes (e.g., matrix metallopeptidase 7 (MMP7) and laminin(gamma 2; LAMC2)) are more likely to be associated with transplantglomerulopathy (FIG. 11B) and together predict transplant glomerulopathywith a higher probability (96% probability in FIG. 11C). A currentquantitative marker of transplant glomerulopathy (the Banff C4d grade)does not identify the subset of transplant glomerulopathy cases thatwere identified via histology. Thus, the BBN-ML considers a morecomplete picture of transplant status (e.g., MMP7 and LAMC2) tocorrectly diagnose the biopsies with transplant glomerulopathy.

A Banff C4d deposition grade of 0.0 has a 44.7% probability of case, anSF probability of 83.9%, and a TG probability of 16.1%. The probabilityof case is the estimated probability of the scenario occurring in thestudy population. A Banff C4d deposition grade of 1.0 has a 24.8%probability of case, an SF probability of 41.9%, and a TG probability of58.1%. A Banff C4d deposition grade of 2.0 has a 19.4% probability ofcase, an SF probability of 53.6%, and a TG probability of 46.4%; and, aBanff C4d deposition grade of 3.0 has a 11.1% probability of case, an SFprobability of 18.8%, and a TG probability of 81.2%. FIG. 13 is a tableillustrating stable function versus transplant glomerulopathy using theLaminin and Matrix Metalloproteinase-7 genes according to an embodimentof the invention. Rather than only having access to two values of C4d(grade 0 or grade 3) to reliably differentiate between stable functionand transplant glomerulopathy, an embodiment of the invention enablesthe clinician to make a diagnosis based on a comprehensive picture ofthe individual patient's current status. This is then interpreted intoan outcome that is described by multiple, interdependent variables (FIG.13). Molecular pathways associated with transplant glomerulopathy arealso identified.

In addition to post-transplant renal biopsies, gene expression changescan be followed in urine sediment, peripheral white blood cells, andrenal allograft biopsies before and during reperfusion to evaluateallografts. In at least one embodiment of the invention, the predicteddisease-specific outcome includes acute rejection, chronic allograftdysfunction, delayed graft function, and/or medium term allograftsurvival likelihoods.

C. Impaired Wound Healing

Another embodiment of the invention inputs data into the BBN-ML topredict the probability of impaired wound healing using biomarkers. Morespecifically, probabilistic predictive networks are utilized to assessthe healing rate of an acute traumatic wound based on the expressionlevel of related biomarkers such as cytokines, chemokines, and/or othergene RNA transcripts and translation products.

Methods are provided for determining wound healing via quantification ofa set of biomarkers, or a subset thereof. Sample biomarkers may includeselected translation products (cytokines and/or chemokines) in apatient's serum and/or wound effluent, as well as RNA transcripts ofselected genes from the patient's wound-bed tissue. A BBN-ML is trainedusing the sample data, which compares a sample biomarker profile to thebiomarker profiles of a patient population with known wound healingoutcomes. An expected wound healing rate and patient-specificprobability of wound outcome are calculated using the BBN-ML.

Cytokine and chemokine expressions provide an insight into the molecularpathogenesis of acute wound failures. The balance between pro- andanti-inflammatory mediators during wound repair is a factor in achievingtissue homeostasis following injury [37]. The inflammatory responsesupplies signals for cellular repair and is the first of severaloverlapping processes that constitute wound healing. However, anexaggerated inflammatory response is deleterious to wound healing. Thepathogenesis of chronic wounds is a failure to progress through thenormal stages of wound healing, wherein the wounds remain in a state ofchronic inflammation [38]. Acute wound failures are the likely result ofa detrimental response to injury. Overproduction of the inflammatorycytokines is seen in posttraumatic inflammation [39-41]. Multiplestudies in trauma populations demonstrate correlations betweeninflammatory cytokines and negative outcomes [42, 43]. Increased IL-6,in particular, is an independent risk factor of morbidity and mortalityin trauma patients [44, 45]. The anti-inflammatory cytokine IL-10 isalso over-expressed in injured patients and is correlated withposttraumatic septic events [46, 47].

In at least one embodiment of the invention, a set of cytokines and/orchemokines are selected as biomarkers for determining wound healingbased on their strong associations with wound outcome. Serum/woundeffluent samples are collected from a patient at different time pointsduring treatment. The levels of biomarkers in each patient serum and/orwound effluent sample are quantified and entered into the BBN-ML forstatistical analysis, which determines the probability of wound outcome.The BBN-ML is constructed using reference biomarker profiles from apatient population with similar wounds and having known wound healingoutcomes. In at least one embodiment, the selected cytokines and/orchemokines include IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7,IL-8, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-15, IP-10, Eotaxin,IFN-γ, GM-CSF, MCP-1, MIP-1α, RANTES, and TNFα, or any subset thereof.

In another embodiment of the invention, the expression profile ofselected genes from a patient wound bed biopsy sample are quantified.The sample may be prepared in a number of ways, as is known in the art,e.g., by mRNA isolation from cells where the isolated mRNA is used asis, amplified, employed to prepare cDNA, cRNA, etc., as is known in thedifferential expression art. The sample is typically prepared from cellsor tissue harvested from a subject to be diagnosed, using standardprotocols, where cell types or tissues from which such nucleic acids maybe generated include any tissue in which the genetic expression patternto be determined exists.

A number of methods for detecting and/or quantifying the expressionlevel of an RNA or protein in a sample are available in the art and canbe employed in the practice of this aspect of the invention. Forexample, hybridization assays including Northern blotting techniques,hybridization to oligonucleotide probe arrays, oligonucleotide probemicroarrays, in situ hybridization, nucleic acid amplification (e.g.,reverse transcriptase-polymerase chain reaction, RT-PCR) and otheranalytical procedures can be employed.

While a variety of different manners of generating expression profilesare known, such as those employed in the field of differential geneexpression analysis, one representative and convenient type of protocolfor generating expression profiles is array-based gene expressionprofile generation protocols. Such applications are hybridization assaysin which a nucleic acid that displays “probe” nucleic acids for each ofthe genes to be assayed/profiled is employed. In these assays, a sampleof target nucleic acids is first prepared from the initial sample ofinterest, where preparation may include labeling of the target nucleicacids via a variety of signal producing system, e.g., coupledfluorescence. Following target nucleic acid sample preparation, thesample is contacted with the array under hybridization conditions,whereby complexes are formed between target nucleic acids andcomplementary to probe sequences that are attached to the array surface.The presence of hybridized complexes is then detected, eitherqualitatively or quantitatively.

In at least one embodiment, expression profiles are generated using anarray of “probe” nucleic acids, which includes a probe for each of thephenotype determinative genes whose expression is contacted with targetnucleic acids, as described above. Contact is carried out underhybridization conditions, and unbound nucleic acid is removed. Theresultant pattern of hybridized nucleic acid provides informationregarding expression for each of the genes that have been probed, wherethe expression information is in terms of whether or not the gene isexpressed and, typically, at what level, where the expression data(i.e., expression profile) may be both qualitative and quantitative.Alternatively, non-array based methods for quantitating the levels ofone or more nucleic acids in a sample may be employed, includingquantitative PCR, and the like. Where the expression profile is aprotein expression profile, any convenient protein quantitation protocolmay be employed, where the levels of one or more proteins in the assayedsample are determined. Representative methods include, but are notlimited to, proteomic arrays, flow cytometry, standard immunoassays,etc.

An embodiment of the invention provides a method for identifyingbiomarkers that impact wound healing. More specifically, the methodstudied U.S. service members that sustained penetrating injuries to oneor more extremities. Up to three wounds per patient were studied.Patients with confounding immunologic co-morbid conditions wereexcluded. Recorded demographic data collected for this exampleembodiment, for example, include age, gender, date, body mass index,nicotine use, injury severity score (ISS), Acute Physiology and ChronicHealth Evaluation II (APACHE-II) scores, concomitant traumatic braininjury, location and mechanism of injury, wound size, associated majorvascular injury to the effected limb, and type of wound closure.

All wounds were examined once daily and patients were followed for 30days. The primary clinical outcome measure was successful wound healingafter definitive closure or coverage with skin graft. Impaired woundhealing included delayed wound closure or wound dehiscence after closureor coverage. Delayed wound closure was defined as definitive closureoccurring 21 days or more after the injury, or two standard deviationsoutside of the mean normal wound closure time period of 10 days.Dehiscence was defined as spontaneous partial or complete wounddisruption after primary closure or greater than 50% skin graft loss.Wounds that progressed to healing at 30 days without necessitating areturn to the operating room were considered healed. Surgicaldebridement, pulse lavage, and VAC application were repeated every 48-72hours until wound closure or coverage, according to currentinstitutional standards of practice. Timing of closure was at thediscretion of the attending surgeon.

Peripheral venous blood (8 mL) was drawn prior to each surgicaldebridement from the patient. Wound effluent samples (30 mL or more)were collected from the VAC canister (without gel pack; KineticConcepts, Inc., San Antonio, Tex.) 2 hours following the first surgicaldebridement and over a 12 hour period prior to each subsequent wounddebridement. All serum samples were immediately separated using acentrifuge at 2500 g for ten minutes. Serum supernatants and effluentsamples were transferred to individually labeled polypropylene tubes(e.g., Cryo-Loc™; Lake Charles Manufacturing, Lake Charles, La.),flash-frozen in liquid nitrogen, and stored at −80° C. until analysis. A1 cm3 wound tissue specimen was obtained from the center of the woundbed at each debridement and immediately stored (e.g., in RNAlater;Ambion, Austin, Tex.) at 4° C.

In at least one embodiment of the invention, serum and wound effluentproteins of interest were quantified using a Luminex® 100 IS xMAP BeadArray Platform (Millipore Corp, Billerica, Mass.). Serum and woundeffluent samples were diluted 2-fold and 100-fold, respectively, andincubated with analyte-specific monoclonal antibodies covalently linkedto uniquely fluorescent beads. Subsequently, biotinylated monoclonalantibodies specific for the bead-linked-antibody: analyte complexes wereintroduced. This secondary complex was then detected bystreptavidin-phycoerythrin. An embodiment of the invention performs thisprocedure using the commercially available Beadlyte® Human 22-PlexMulti-Cytokine Detection System (Millipore, Billerica, Mass.) accordingto manufacturer's instructions and using the sample dilutions specifiedabove.

Twenty-two cytokines and chemokines (IL-1α, IL-1β, IL-2, IL-3, IL-4,IL-5, IL-6, IL-7, IL-8, IL-10, IL-12(p40), IL-12(p70), IL-13, IL-15,IP-10, Eotaxin, IFN-γ, GM-CSF, MCP-1, MIP-α, RANTES, and TNFα) werequantified using a detection system (e.g., Beadlyte® Human 22-PlexMulti-Cytokine Detection System; Cat.# 48-011; Upstate/Millipore,Billerica, Mass.). RNA transcripts and translated proteins of IL-1α,IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12(p40),IL-12(p70), IL-13, IL-15, IP-10 (CXCL10), Eotaxin (CXCL11), IFN-γ,GM-CSF (CSF2), MCP-1 (CCL2), MIP-1α (CCL3), RANTES (CCL5), and TNFα inpatients with traumatic wounds were examined as potential biomarkers ofwound healing and indicators of proper time of wound closure orcoverage. As illustrated in FIG. 14, the expressions of a total 190genes are studied. Gene names are provided in FIG. 15A-15J.

RNA was extracted from a biopsy sample of the wound bed and converted tocDNA by standard extraction and reverse transcription techniques withadherence to common quality assurances. Quantitative polymerase chainreaction (qPCR) was performed to calculate the relative expressions ofthe array of gene targets (see FIG. 14). An embodiment of the inventionused a real-time thermocycler, 7900HT Fast Real-Time PCR System (AppliedBiosystems, Foster City, Calif.) and a custom designed panel of 190primer:probe sets for target genes arranged in duplicate in a customformat of a TaqMan Low Density Array (Applied Biosystems, Foster City,Calif.) to assess gene transcript expression. An 18S rRNA-specificprimers:probe set was included in quadruplet on this array to serveprimarily as an internal calibrator.

Associations between categorical variables were studied with Fisherexact test or _(χ)2test, as appropriate. Continuous variables wereassessed with the Mann-Whitney U-test and Kruskal-Wallis test formultiple comparisons. Wounds were considered independently forstatistical analysis of analyte expression and healing outcome. Anembodiment of the invention performs statistical analysis using SPSS(Version 16.0, SPSS Inc., Chicago, Ill.). A two-tailed p value of lessthan 0.05 was considered statistically significant. Both systemic andwound-specific extents of injury are clinical determinants of thesubsequent wound healing outcome. As illustrated in FIG. 16, significantrisk factors for impaired wound healing include elevated Injury SeverityScore (ISS) (p=0.001), larger wound size (p=0.03), and associatedvascular injury (p=0.016). Age, body mass index, tobacco use, mechanismof injury, wound location, traumatic amputation, and closure method arenot associated with wound healing (p>0.05).

As illustrated in FIG. 17, in addition to increased ISS, patients withimpaired wound healing also demonstrated sustained physiologicderangement with elevated APACHE II scores at each surgical debridement.Mean APACHE II scores are statistically higher over the first threedebridements (p<0.05) and remain elevated at wound closure in patientswith impaired wound healing. Thus, the severity of systemic illnessfollowing traumatic injury is associated with decreased capacity forwound healing.

Serum cytokine and chemokine expression is associated with impairedwound healing. As illustrated in FIG. 18, the physiologic response toacute traumatic injury is reflected in the serum cytokine and chemokinedata. The pro-inflammatory cytokine IL-6 is statistically higher at eachsurgical debridement in patients with impaired healing compared topatients with normal healing (p<0.05). The anti-inflammatory cytokineIL-10 is statistically higher from the second debridement until woundclosure in this comparison (p<0.05). The chemokine MCP-1 is consistentlyhigher while IP-10 is higher at the second debridement (p<0.05). Thecytokines IL-5 and IL-7 are significantly increased at the seconddebridement in the impaired healing patients (p<0.05) (data not shown).All other analytes do not differ between the groups. Thus, adysregulated inflammatory systemic response is observed in patients withimpaired wound healing.

Wound VAC effluent cytokine and chemokine expression is associated withimpaired wound healing. As illustrated in FIG. 19, patients withimpaired wound healing express statistically higher initial woundeffluent IL-6, IL-10, MCP-1, and MIP-1α compared to patients with normalhealing (p<0.05). Moreover, patients with impaired wound healing expressstatistically lower IL-5 at the third and final debridements (p<0.05).Thus, similar to the systemic condition, a wound-specific dysregulatedinflammatory response is observed in wounds with impaired wound healing.

Tissue biopsies from healed and dehisced wounds are analyzed over alldebridements. Wounds with impaired healing express higher inflammatorycytokines and chemokines compared to healed wounds. As illustrated inFIGS. 20A-B, these findings suggest that acute wound failure representsunresolved inflammation at the molecular level and subsequent failure toprogress through the normal phases of wound healing. Receiver operatingcharacteristics for individual serum and effluent biomarkers areanalyzed over all wound debridements. Serum IL-6, IL-8, IL-10, MCP-1 andIP-10, and effluent RANTES are statistically predictive of wound healingoutcome (p<0.05). Specifically, serum IL-10 has an AUC of 0.902 and a pvalue of less than 0.001; and, serum MCP-1 has an AUC of 0.889 and a pvalue of less than 0.001. Serum IL-6 has an AUC of 0.886 and a p valueof less than 0.001; and, serum IP-10 has an AUC of 0.865 and a p valueof less than 0.001. Serum IL-8 has an AUC of 0.815 and a p value of0.003; and, effluent RANTES has an AUC of 0.790 and a p value of 0.006.

An embodiment of the invention provides a BBN-ML for estimating woundhealing outcomes using cytokine and chemokine biomarker expressionlevels. As described above, inflammatory wound effluent cytokine andchemokine expression at time of initial wound debridement is correlatedwith wound outcome. Patients with subsequent wound dehiscence expresssignificantly higher initial effluent inflammatory cytokines andchemokines (IL-1β, IL-6, IL-8, IL-10, and MCP-1) compared to patientswith normal healing. Corroboratively, wound biopsy gene expression ofinflammatory cytokines and chemokines is elevated in dehisced woundsrelative to healed wounds. Furthermore, serum IL-6 and IL-10, andeffluent IL-5 and RANTES are individually predictive of wound healingoutcome as indicated by their receiver-operating characteristics.

However, in a complex physiologic system, it is unusual that anindividual biomarker is robustly predictive of any outcome. In addition,based on the initial observations, cytokine and chemokine profiles thatfavor wound healing dynamically change throughout the healing (marked bydebridements) process (see FIG. 18). To describe these complex,non-obvious relationships over time, the BBN-ML is used to relate serumand effluent cytokine and chemokine concentrations to expected woundhealing outcome at each surgical debridement.

Multivariate conditional dependence relationships are identified usingBayesian modeling software (e.g., FasterAnalytics™). Bayesianprobability theory relates the conditional probabilities of two or morerandom events in order to compute posterior probabilities and has beenused previously in clinical diagnostics [38, 48-49]. A probabilisticmodel is generated using the serum and wound effluent cytokine andchemokine protein data at each surgical debridement from healed patientpopulation. A step-wise training process is used to prune includedfeatures of the Bayesian network to improve model robustness andelucidate the cytokines/chemokines with the most significant influenceson wound outcome. Serum MCP-1, IP-10, and IL-6 and effluent MCP-1, IL-5,and RANTES are highly predictive of outcome. As illustrated in FIG. 18,impaired healing is associated with high serum MCP-1, IP-10, IL-6 andeffluent MCP-1 and low effluent IL-5 and RANTES at various wounddebridements. As early as the first debridement, wound healing outcomeis determined based on serum and effluent MCP-1, serum IL-6 and effluentIL-5 expression (FIG. 18).

FIG. 21A illustrates a BBN-ML 2100 for predicting wound healing 2110(either impaired or normal), according to an embodiment of theinvention. Each node in the BBN-ML 2100 represents a biomarker level2120A-2120L: IL-6 from serum at day 3; MCP-1 from serum at day 1; IL-6from serum at day 1; RANTES from wound effluent at day 3; IP-10 fromserum at day 3; IP-10 from serum at day 2; MCP-1 from wound effluent atday 1; IL-5 from wound effluent at day 3; MCP-1 from serum at day 2;MCP-1 from serum at day 30; IP-10 from serum at day 1; and, IL-5 fromwound effluent at day 30, respectively. Specifically, each noderepresents the serum or wound effluent concentration (quantitated asmean fluorescence intensity) of the individual cytokine or chemokinebiomarker 2120A-2120L at specific time points along the healingtrajectory. FIG. 21B illustrates the receiver-operating characteristicsfor the cross validation of the BBN-ML 2100. The receiver-operatingcharacteristics show that the BBN-ML 2100 is very robust with an AUC of0.872.

As described above, the predictive models of the embodiments herein arein an interactive format when used in conjunction with a GUI, such thatclinicians can enter known values for a current patient (e.g., byclicking on the cross-hatched bars in the model nodes) and receive aestimate of the probability of healing impairment. Thus, as illustratedin FIG. 21C, if on day 1, MCP1 in the wound effluent and MCP-1, IL-6,and IP-10 in the serum are high (nodes 2100B, 2100C, 2100G, and 2100K),then there is a 76% likelihood of wound healing impairment. However, ifthese same proteins are low, as illustrated in FIG. 21D, then there isan 87% probability of normal wound healing.

In at least one embodiment, a K-fold cross-validation is used to testthe BBN-ML. Cross-validation by number of patients (19) is performed toavoid modeling bias by omitting all serum data from the training setthat would appear in the testing set. A ROC curve of these predictionsis calculated to determine model robustness for predicting wound healingoutcome. In a K-fold cross-validation, the original sample ispartitioned into K subsamples. Of the K subsamples, a single subsampleis retained as the validation data for testing the predictive models,and the remaining K—1 subsamples are used as training data. Thecross-validation process is repeated K times (the folds), with each ofthe K subsamples used exactly once as the validation data. The K resultsfrom the folds are averaged (or otherwise combined) to produce a singleestimation. Contrary to repeated random sub-sampling, all observationsare used for both training and validation, and each observation is usedfor validation exactly once.

FIG. 21E illustrates a naive BBN for predicting the probability of woundhealing (dehisced or healed) according to an embodiment of theinvention, wherein relative contribution of the predictors 2130A-2130Jinclude 4 bins (i.e., levels of gene expression). Specifically, woundhealing outcome 2110 is dependent upon the gene expression levels ofbiomarkers IL-10, IFNg, IL-12p40, IL-2, RANTES, Eotaxin, IL-15, IL-13,TNFα, and MCP-1. FIG. 21F illustrates an interim BBN for predicting theprobability of wound healing according to an embodiment of theinvention. Specifically, wound healing outcome 2110 (“Outcome_fin”) isdependent upon the gene expression levels of biomarkers IL-8₁₃ S_1,MCP-1a_S_3, MIP-1a_S_1, MIP-1a_S_3, IL-12p40_S_3, and IL-6_S_3(predictors 2140A-2140F, respectively). The final BBN-ML is illustratedin FIG. 21G according to an embodiment of the invention, wherein each ofthe predictors 2150A-2150E includes two gene expression level bins. Thepredictors 2150A and 2150C each have a lower bin of less than or equalto 17.9 and an upper bin of greater than 17.9. The predictor 2150B has alower bin of less than or equal to 691 and an upper bin of greater than691; and, the predictor 2150D has a lower bin of less than or equal to27.2 and an upper bin of greater than 27.2.

As illustrated in FIG. 21B, the cross-validation of the BBN-ML showedthat the model was robust and effectively estimated wound healingoutcome (AUC=0.872, p=0.002). The Bayesian Belief Network (i.e., theBBN-ML) has a sensitivity of 88.9%, a specificity of 90%, an impairedpredictive value of 80%, and a normal predictive value of 94.7%. Anembodiment of the invention prospectively validates the BBN-ML using denovo biomarker data of patients with unknown wound healing outcome.Eighty (80) patients were recruited from adult male and females withextremity wounds (including the shoulder and buttock), which may betreated by VAC. The patients had an Injury Severity Score of 9, aminimum wound size greater or equal to 75 cm2, and were at least 18years old. To reduce the risk of clinical trails, patients with CoronaryArtery Disease, Diabetes Mellitus (IDDM or T2DM), Peripheral VascularDisease, connective tissue disorders, immunosuppression, pregnancy, orage greater than 65 years old were excluded from the study.

The patients were separated into two arms. For the control arm (n=40),samples were collected but surgical wound closure was based on theattending surgeon's discretion. For the experimental Arm (n=40), sampleswere collected, analyzed with the BBN-ML, and surgical wound closure wasbased upon the BBN-ML's prediction. Wounds with greater than 70%probability to heal were closed. Wound biopsies, effluent and serumcollections were obtained at each wound exploration and washout, whichwere expected to occur within 24 hours of each patient's admission andevery 48-72 hours subsequently. Wound biopsies were processed forquantitative real-time PCR using a low-density array; and,effluent/serum proteins were quantitated using a multiplexantibody-based assay. Biomarker expression levels were used as evidencein the BBN-ML of wound healing to obtain the predicted wound outcome ifthe wound was to be surgically closed during the following operatingroom visit.

Data analysis was performed using traditional (Frequentist) statisticsand Bayesian statistics. Under traditional statistical analysis,associations between categorical variables were evaluated with Fisherexact test or _(χ)2 test, as appropriate. Continuous variables wereassessed with the Mann-Whitney U-test and Kruskal-Wallis test formultiple comparisons. Wounds were considered independently forstatistical analysis of analytes (cytokines/chemokines) expression andhealing outcome. ROC curves were constructed by plotting sensitivityversus 1—specificity, and the AUC was calculated to ascertain thepredictive value of the cytokine and chemokine biomarker profiles. Anembodiment of the invention performs statistical analysis using SPSS(Version 16, SPSS Inc., Chicago, Ill). A two-tailed p value <0.05 isconsidered statistically significant.

For the training of the BBN-ML model, the data generated from theclinical studies along with clinical parameters were collected in acommon database. This data was reviewed for accuracy and usability. Thedata was analyzed using the BBN-ML to identify conditional dependencebetween clinical outcomes and specific surrogate biomarker profiles toestablish a model of biomarker/outcomes dependency and quantitative,patient-specific risk stratification.

The BBN-ML allows surgeons to use a quantitative, reliable method forwound assessment rather than subjective methods in current practice,which can reduce surgeon-to-surgeon variability involved in determiningthe proper time for definitive wound closure. Using the patient'sbiomarker values, the BBN-ML provides the surgical team with an estimateof wound-healing rate and the likelihood of healing success if the woundwere to be closed. Providing such a quantitative and objective measureof wound status greatly reduces intra-observer variability and improvespersonalized, and in some cases wound-specific, treatment of traumapatients. The BBN-ML has machine-learning capabilities in that theaccuracy of the BBN-ML improves with each additional patient's biomarkerinformation entered into the database. Thus, data from the clinicalstudy is collected for model refinement.

For the training of the BBN-ML model, the data generated from theclinical studies along with clinical parameters were collected in acommon database. This data was reviewed for accuracy and usability. Thedata was analyzed using the BBN-ML to identify conditional dependencebetween clinical outcomes and specific surrogate biomarker profiles toestablish a model of biomarker/outcomes dependency and quantitative,patient-specific risk stratification.

The BBN-ML allows surgeons to use a quantitative, reliable method forwound assessment rather than subjective methods in current practice;this should reduce surgeon-to-surgeon variability involved indetermining the proper time for definitive wound closure. Using thepatient's biomarker values, the BBN-ML provides the surgical team withan estimate of wound-healing rate and the likelihood of healing successif the wound were to be closed. Providing such a quantitative andobjective measure of wound status greatly reduces intra-observervariability and improves personalized, and in some cases wound-specific,treatment of trauma patients. The BBN-ML has machine-learningcapabilities in that the accuracy of the BBN-ML improves with eachadditional patient's biomarker information entered into the database.Thus, data from the clinical study is collected for model refinement.

The BBN-MLs support several scoring metrics for network optimization:Minimum Description Length (MDL), also known as the Bayesian InformationCriterion (BIC), as well as Bayesian Scoring (BDe). Minimum DescriptionLength scoring provides a measure of quality of a model. It trades offbetween goodness-of-fit and model complexity. Goodness-of-fit ismeasured as the likelihood of the data given the model. Model complexityequals the amount of information required to store the model, subject toan inflator/deflator set by the user. Bayesian Scoring is asymptoticallyequivalent to MDL scoring. MDL scoring ensures that the final modelrepresents the most likely model given the data used for learning andthe model variations under consideration.

D. Breast Cancer Risk

Another embodiment of the invention inputs data into a BBN-ML to predictbreast cancer risk. 591 female military healthcare beneficiaries wereenrolled into an IRB-approved, single-arm, prospective pilot screeningtrial between August 2002 and March 2005. The clinical protocol wasreviewed and approved by the Institutional Review Boards of Walter ReedArmy Medical Center (WRAMC), Washington, D.C. and Keller Army Hospital(KAH), West Point, N.Y. Subjects were recruited from the gynecologyclinic or Comprehensive Breast Center at WRAMC or the gynecology orfamily practice clinic at KAH. Study inclusion criteria consisted ofyounger women age 18 to 49 years. Age was stratified for analysis asfollows (<30, 30-34, 35-39 and 40-49). Potential study subjects wereexcluded if: they had breast surgery (including core biopsy) or werelactating within the preceding 3 months, had breast fine needleaspiration within the preceding one-month, were pregnant, hadelectrically powered implanted devices (e.g. pacemaker) or wereundergoing chemotherapy or radiation treatment. Data collected for eachstudy subject included age, race/ethnicity, clinical history (personaland family history of breast cancer, previous breast surgery or biopsyand results of those interventions), hormonal information (age ofmenarche and first full-term pregnancy, menstrual status, date of lastmenstrual period and exogenous hormone use), breast density and size(bra cup size), Gail Model risk estimate, results of clinical breastexam (CBE), screening breast electrical impedance scanning (EIS),conventional imaging and biopsy results. The study participantsunderwent EIS of the breast (e.g., using the T-Sca™ 2000ED (MirabelMedical, Austin, Tex.)) [69].

In at least one embodiment of the invention, the BBN-ML is trained usinga priori variables to estimate the likely diagnostic outcome of breastbiopsy. The BBN-ML is developed using machine learning algorithms (e.g.,FasterAnalytics™), which automatically learn network structure and jointprobabilities from the prior probabilities in the data. BBN models are atype of directed acyclic graph, which means that they representinformation in a hierarchical format, which identifies variables whichcontain the most information and are thus most useful for estimatingoutcomes. The associations represented by the BBN-ML are associations ofconditional dependence.

An embodiment of the invention performs cross-validation on the BBN-MLusing a train-and-test cross-validation methodology to produceclassification accuracy estimates. Five-fold cross validation isperformed by randomizing the data set into 5 separate and uniquetrain-and-test sets. Each set consists of a training set comprised of80% of patient records and a test set consisting of the remaining 20% ofrecords. Once the BBN-ML is constructed with a training set, thematching test set is entered into the BBN-ML, generating a case-specificprediction for each record for independent variables of interest. A ROCcurve is plotted for each test to calculate classification accuracy. TheROC curve is used to calculate the AUC and corresponding predictivevalues for biopsy outcome.

In at least one embodiment, the study population was comprised of anethnically diverse group of younger women (41% non-Caucasian). FIG. 22is a table illustrating characteristics of the study population by age;and, FIG. 23 is a table illustrating characteristics of the studypopulation by biopsy. Of the 591 study participants, 67% were under theage of 40 (mean age: 35 ± 6.9 years), and 90% pre-menopausal. Twopercent of the study population was taking exogenous hormones at thetime of study enrollment; however, there was no statisticallysignificant association with disease (x²=0.95). Fifty-five percent ofparticipants had no family history of breast cancer, and family historywas only marginally associated with biopsy outcome (x²=0.10). Thefindings of CBE were statistically associated with both age (x²=0.01)and disease (x²<0.001); 31% of subjects had findings that were deemednot suspicious, while 4% of subjects had suspicious CBE findings. Fivepercent of study subjects had estimated 5-year risk of breast cancer≥1.66% according to the Gail Model and these findings were statisticallyassociated with both disease and age of subject (x²<0.001). Mammographywas performed in 281 women and found to be BIRADS III or higher in 75cases (27%), while mammography was found to be statistically associatedwith both disease and age of subject (x²<0.001). Breast ultrasoundexamination was performed in 258 women and found to be BIRADS III orhigher in 66 cases (26%); ultrasound was statistically associated withdisease (x²<0.001), but not with age (x²=0.18). Three risk factors werenot statistically associated with biopsy outcome: mean age at menarche(p=0.12), mean age at first pregnancy (p=0.39), and nulliparity(x²=0.93). There was no statistically significant difference between themean age of the study population (35 years) and the mean age at time ofcancer diagnosis (38 years, x²=0.35), or diagnosis of pre-malignanthistopathology (38 years, x²=0.56). Data is tabulated by age group andbiopsy outcome as shown in FIGS. 22-23.

Of the 591 women enrolled in the study, 568 screened EIS negative (lowrisk) and 23 EIS positive (high risk). In the EIS negative group, 95underwent biopsy and 87 were benign on final histopathology. The eightremaining women were either pre-malignant (n=4) or malignant (n=4). Inthe EIS positive group, 10 underwent biopsy and five were benign, whilefive were either pre-malignant (n=3) or malignant (n=2). Of 13pre-malignant or malignant lesions, EIS identified five (38.5%). The NPVof the EIS negative group was 92%, while the PPV of the EIS positivegroup was 50%.

FIG. 24 illustrates a BBN-ML for predicting breast cancer risk accordingto an embodiment of the invention. The six nearest independentassociated features (direct relationship to breast biopsy diagnosis), inthe illustrated BBN-ML used to estimate a breast biopsy diagnosis(Biopsy Category 2400) are: screening breast EIS result 2410A, Gailmodel cutoff 2410B (5-year risk estimate <1.66% vs. ≥1.66%), MMG BIRADresult 2410C (mammogram) and MRI BIRAD result 2410D, breast size 2410E,and personal history of breast disease 2410F. This does not mean,however, that ‘Any Palpable Mass’ on clinical breast examination 2410Gand ultrasound (US) BIRAD result 2410H (indirect relationship to breastbiopsy diagnosis 2400) do not influence the estimate of likely biopsydiagnosis, but rather that they are conditionally independent of biopsyoutcome given knowledge of screening breast EIS 2410A and MMG BIRADresult 2410C.

The illustrated BBN-ML was validated using train-and-testcross-validation, and produced strongly predictive AUCs (0.75-0.97) fordifferentiating malignancy and premalignant disease from benign findings(FIG. 25). Specifically, the BBN-ML has ROC curves, when crossvalidated, with AUCs of 0.88, 0.97 and 0.75 for benign, malignant, andpremalignant findings, respectively. Cross-validation also produces a97% NPV and a 42% PPV for malignancy. With a relatively small set ofoutcomes, there is a high degree of variance in results betweencross-validation exercises (FIG. 25). The BBN-ML is a recursiveinformation structure, and the inclusion of conditional dependencebetween predictive variables guards against over-interpretation of data(over-fitting). The BBN-ML informs estimates not only through estimationof biopsy outcome, but simultaneously through estimation of as-yetunknown imaging results, wherein estimates of biopsy outcome are derivedfrom available clinical and imaging data, even if some imaging studiesare unavailable at time of biopsy outcome estimation.

FIGS. 26A-C illustrate a BBN-ML for predicting breast cancer riskaccording to an embodiment of the invention, including clinicalparameters 2410A-2410H. Knowledge of breast size 2410E (bra cup B)results in slightly lower risk of cancerous biopsy result (−3.6%) forthe test subject compared to the study population (FIG. 26A). When theadditional knowledge of Gail model cutoff 2410B is added (FIG. 26B) torefine the posterior estimate of biopsy outcome 2400 given previouslyknown breast size 2410E, there is a 12% increased likelihood ofcancerous biopsy, and a 17% increase in the likelihood of pre-malignanthistology, relative to the overall study cohort. Moreover, adding apositive (high risk) EIS screening result 2410A (FIG. 26C) increases theposterior risk estimate of cancerous biopsy by 21%, and the riskestimate of pre-malignant disease by 35%.

As clinical parameters 2410A-2410H are, at some level, conditionallydependent with biopsy outcome 2400, the clinical parameters 2410A-2410Hthat are available at the time of initial clinical visit (a prioriknowledge) are selected and applied to the BBN-ML to estimate biopsyoutcome. Subsets of the clinical parameters 2410A-2410H are also used togenerate an inference table (FIG. 27) that can be used by clinicians toquickly estimate biopsy outcome for all known combinations of theidentified clinical parameters 2410A-2410H if the GUI interface to theBBN-ML is unavailable. The incremental value of both screening breastEIS 2410A and Gail model cutoff 2410B is shown in the FIG. 27. Under themost favorable circumstances (EIS negative and Gail model 5-year risk<1.66%), the risk of malignancy is 2.6%. Under the least favorablecircumstances (screening EIS positive and Gail model 5-year risk≥1.66%),the risk of malignancy is 45%.

In at least one embodiment of the invention, screening breast EIS result2410A, Gail model cutoff 2410B, MMG BIRAD result 2410C and MRI BIRADresult 2410D, breast size 2410E, and personal history of breast disease2410F are each related to biopsy category 2400 when examined using thecalculated chi square. CBE 2410G and breast ultrasound results 2410H arealso statistically associated with biopsy outcome, and the BBN-MLincludes these features as well, but they are associated with biopsyoutcome through EIS and mammography results. Other features that havebivariate statistical significance, but are not included in the BBN-MLillustrated in FIGS. 26A-C, include patient ethnicity, menopausalstatus, and prior breast biopsy. The machine learning process produces aparsimonious BBN-ML; thus, in at least one embodiment, these additionalfactors are not included in favor of more specific imaging and personalhistory risk factors. However, as the training population database isenhanced and enlarged, the BBN-ML will be retrained and the structureand priority of the features in the BBN-ML may be revised to morerobustly reflect the clinical population.

In an embodiment of the invention, a number of attributes havingstatistically significant association with patient age, including CBEfindings, mammography BIRADS category, nulliparity, and Gail Model5-year risk score. Conversely, certain factors that are significantlyassociated with biopsy outcome using both bivariate statistical testsand the machine-learning process, are not associated with patient age:breast (bra cup) size, EIS screening exam, or MRI. There is nostatistically significant difference in mean age at diagnosis ofpre-malignant or malignant disease compared to the mean age of the studypopulation. When biopsy results are examined by age category, theresults do not demonstrate any statistically significant associations.Additionally, features generally considered as well established breastcancer risk factors in the general population are not statisticallysignificant with biopsy outcome in the younger test population,including family history of breast cancer, age at menarche, nulliparity,and age at first pregnancy.

In at least one embodiment, the BBN-ML not only allows the posteriorestimation of the likely biopsy outcome, but also identifies a hierarchyof conditional dependence, which identifies which pieces of informationare most useful in calculating the estimate. This hierarchy also defineshow independent variables influencing biopsy outcome also influence oneanother, providing a better understanding of how the estimate is derivedand providing an opportunity to estimate missing parameters using thosecurrently available for any given patient. Because this hierarchy istrained using fully unsupervised machine learning, the hierarchy willchange over time as knowledge is accrued. The combined effect of theseindependent predictors on likelihood of disease is greater than the sumof the individual effects. By way of example, in one embodiment,mammography finding of BIRAD IV increases the likelihood of a malignantbiopsy result in the study population by five percent, while a Gail5-year risk score >1.66% increases the likelihood of malignancy by 26%,yet together these findings increase the likelihood of disease by42%—greater than the sum total of their individual effects.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the root terms “include”and/or “have,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans plus function elements in the claims below are intended to includeany structure, or material, for performing the function in combinationwith other claimed elements as specifically claimed. The description ofthe present invention has been presented for purposes of illustrationand description, but is not intended to be exhaustive or limited to theinvention in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art without departing fromthe scope and spirit of the invention. The embodiment was chosen anddescribed in order to best explain the principles of the invention andthe practical application, and to enable others of ordinary skill in theart to understand the invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

The invention can take the form of an entirely hardware embodiment or anembodiment containing both hardware and software elements. In at leastone exemplary embodiment, the invention is implemented in a processor(or other computing device) loaded with software, which includes but isnot limited to firmware, resident software, microcode, etc.

A representative hardware environment for practicing at least oneembodiment of the invention is depicted in FIG. 28. This schematicdrawing illustrates a hardware configuration of an informationhandling/computer system in accordance with at least one embodiment ofthe invention. The system includes at least one processor or centralprocessing unit (CPU) 10. The CPUs 10 are interconnected via system bus12 to various devices such as a random access memory (RAM) 14, read-onlymemory (ROM) 16, and an input/output (I/O) adapter 18. The I/O adapter18 can connect to peripheral devices, such as disk units 11 and tapedrives 13, or other program storage devices that are readable by thesystem. The system can read the inventive instructions on the programstorage devices and follow these instructions to execute the methodologyof at least one embodiment of the invention. The system further includesa user interface adapter 19 that connects a keyboard 15, mouse 17,speaker 24, microphone 22, and/or other user interface devices such as atouch screen device (not shown) to the bus 12 to gather user input.Additionally, a communication adapter 20 connects the bus 12 to a dataprocessing network 25, and a display adapter 21 connects the bus 12 to adisplay device 23 which may be embodied as an output device such as amonitor, printer, or transmitter, for example.

Computer program code for carrying out operations of the presentinvention may be written in a variety of computer programming languages.The program code may be executed entirely on at least one computingdevice (or processor), as a stand-alone software package, or it may beexecuted partly on one computing device and partly on a remote computer.In the latter scenario, the remote computer may be connected directly tothe one computing device via a LAN or a WAN (for example, Intranet), orthe connection may be made indirectly through an external computer (forexample, through the Internet, a secure network, a sneaker net, or somecombination of these).

It will be understood that each block of the flowchart illustrations andblock diagrams and combinations of those blocks can be implemented bycomputer program instructions and/or means. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, application specific integratedcircuit (ASIC), or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions specified in theflowcharts or block diagrams.

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1-7. (canceled)
 8. A method of generating a model predicting apersonalized risk of disease for a subject, comprising: a) generating,by a processor, at least one fully unsupervised Bayesian Belief Network(BBN) model using data from a training database, wherein i) the trainingdatabase comprises a set of reference clinical parameters obtained froma plurality of subjects having known disease outcomes, and ii) the fullyunsupervised BBN model comprises a directed acyclic graph including aplurality of nodes, wherein each node includes at least two bins, eachbin representing a value range of a clinical parameter associated withthat node, and wherein each of the nodes comprises data identifying atleast one conditional dependence relationship between a known diseaseoutcome and the clinical parameter associated with that node; b)inputting a set of clinical parameters into the fully unsupervised BBNmodel, wherein the set comprises clinical parameters of a samplecollected from an individual subject; c) generating, by the processor, apredicted clinical outcome comprising a subject-specific probability ofdeveloping and/or recovering from a disease for the individual subject,using the fully unsupervised BBN model as a classifier, wherein theclassifier is a machine learning system that compares the clinicalparameters of the sample with the set of reference clinical parametersof the plurality of subjects having known disease outcomes; and d)displaying and/or outputting the predicted clinical outcome.
 9. Themethod of claim 8, wherein the predicted clinical outcome displayedand/or output in step d) is provided in an interactive format using agraphical user interface configured to: allow a user to select apotential clinical outcome and/or clinical parameters for the sampleinput in step b), and to update a probability distribution for each ofthe remaining variables used to generate the BBN model, in response tothe user's selection of the potential clinical outcome and/or clinicalparameters.
 10. The method of claim 8, wherein the fully unsupervisedBBN model is generated without human-developed decision support rules.11. The method of claim 8, wherein the set of clinical parameters usedin steps a), b) and/or c) comprises biomarker levels collected from atleast one of serum or biopsy tissue, the biomarker levels including geneexpression levels for an IP-10 gene, IL-6 gene, MCP-1 gene, IL-5 gene,and a RANTES gene.
 12. The method of claim 8, further comprisingestimating an accuracy level of the subject-specific probability ofdeveloping and/or recovering from a disease, the accuracy levelcomprising at least one of model sensitivity, model specificity,positive and negative predictive values, and/or overall accuracy. 13.The method of claim 8, wherein the disease comprises: a) a breastcancer; b) a thyroid malignancy; or c) a transplant glomerulopathy. 14.The method of claim 13, wherein the disease comprises a breast cancerand the clinical parameters comprise one or more of a Gail model cutoff,mammogram results, MRI results, breast size, and personal history ofbreast disease.
 15. The method of claim 13, wherein the diseasecomprises a breast cancer and the clinical parameters compriseultrasound data and results of a clinical breast examination.
 16. Themethod of claim 13, wherein the disease comprises a thyroid malignancyand the clinical parameters comprise functional status of a thyroidnodule, number of cervical lymph nodes, serum thyrotropin level,pre-operative diagnosis, nuclear medicine rating, age, and ethnicity.17. The method of claim 13, wherein the disease comprises a transplantglomerulopathy and the clinical parameters comprise gene expressionlevels for an ICAM-1 gene, IL-10 gene, CCL-3 gene, CD-86 gene, CCL-2gene, CXCL-11 gene, CD-80 gene, GNLY gene, and PRF-1 gene.
 18. Themethod of claim 13, wherein the disease comprises a transplantglomerulopathy and the clinical parameters comprise gene expressionlevels for a CD40LG gene, IFNG gene, CD-28 gene, CXCL-10 gene, CCR-5gene, CD-40 gene, CTLA-4 gene, TNF gene, CXCL-9 gene, CX3CR-1 gene,FOXP-3 gene, EDN-1 gene, CD-4 gene, TBX-21 gene, FASLG gene, C-3 gene,CD3E gene, CXCR-3 gene, and CCL-5 gene.
 19. The method of claim 13,wherein the disease comprises a transplant glomerulopathy and theclinical parameters comprise gene expression levels for a VCAM1 gene,MMP9 gene, Banff C4d gene, MMP7 gene, and LAMC2 gene.
 20. The method ofclaim 13, wherein the disease comprises a transplant glomerulopathy andthe clinical parameters comprise gene expression levels for a TNC gene,S100A4 gene, NPHS1 gene, NPHS2 gene, AFAP gene, PDGF8 gene, SERPINH1gene, TIMP4 gene, TIMP3 gene, VIM gene, SERPINE1 gene, TIMP1 gene, FN1gene, ANGPT2 gene, TGFB1 gene, ACTA2 gene, TIMP2 gene, COL4A2 gene, MMP2gene, COL1A1 gene, COL3A1 gene, GREM1-2 gene, SPARC gene, IGF1 gene,SMAD3 gene, HSPG2 gene, FN1 gene, ANGPT2 gene, TGFB1 gene, ACTA2 gene,THBS1 gene, CTNNB1 gene, FGF2 gene, TJP1 gene, FAT gene, CDH1 gene,SMAD7 gene, CD2AP gene, CDH3 gene, CTGF gene, ACTN4 gene, SPP1 gene,AGRN gene, VEGF gene, and BMP7 gene.
 21. A method for determining apatient-specific probability of impaired wound healing, said methodincluding: a) generating, by a processor, at least one fullyunsupervised Bayesian Belief Network (BBN) model using data from atraining database, wherein i) the training database comprises a set ofreference clinical parameters obtained from a plurality of subjectshaving known wound healing outcomes, the set of clinical parameterscomprising gene expression levels for a plurality of genes, and ii) thefully unsupervised BBN model comprises a directed acyclic graphincluding a plurality of nodes, each node comprising at least two binswith each bin representing a value range of a clinical parameterassociated with that node, wherein each of the nodes comprises dataidentifying at least one conditional dependence relationship between theknown wound healing outcomes and the clinical parameter associated withthat node; b) inputting a set of clinical parameters into the fullyunsupervised BBN model, wherein the clinical parameters are associatedwith a sample comprising at least one of serum, wound effluent, orbiopsy tissue collected from an individual subject; c) generating, bythe processor, a predicted clinical outcome comprising asubject-specific probability of impaired wound healing for theindividual subject, using the fully unsupervised BBN model as aclassifier, wherein the classifier is a machine learning system thatcompares the clinical parameters of the sample with the set of referenceclinical parameters of the plurality of subjects having known woundhealing outcomes; and d) displaying and/or outputting the predictedsubject-specific probability of impaired wound healing.
 22. The methodof claim 21, wherein the predicted clinical outcome displayed and/oroutput in step d) is provided in an interactive format using a graphicaluser interface configured to: allow a user to select a potentialclinical outcome and/or clinical parameters for the sample input in stepb), and to update a probability distribution for each of the remainingvariables used to generate the BBN model, in response to the user'sselection of the potential clinical outcome and/or clinical parameters.23. The method of claim 21, wherein the fully unsupervised BBN model isgenerated without human-developed decision support rules.
 24. The methodof claim 21, further comprising estimating an accuracy level of thesubject-specific probability of wound healing, the accuracy levelcomprising at least one of model sensitivity, model specificity,positive and negative predictive values, and/or overall accuracy. 25.The method of claim 21, wherein the clinical parameters used in stepsa), b) and/or c) comprises gene expression levels for an IP-10 gene,IL-6 gene, MCP-1 gene, IL-5 gene, and a RANTES gene.
 26. The method ofclaim 21, wherein the clinical parameters used in steps a), b) and/or c)comprises gene expression levels for an IL-1α gene, IL-10 gene, IL-2gene, IL-3 gene, IL-4 gene, IL-7 gene, IL-8 gene, IL-10 gene, IL-12(p40)gene, IL-12(p70) gene, IL-13 gene, IL-15 gene, Eotaxin gene, IFN-γ gene,GM-CSF gene, MIP-60 gene, and TNFα gene.
 27. The method of claim 21,wherein the clinical parameters used in steps a), b) and/or c) furthercomprises levels of RNA transcripts and translation products of one ormore genes selected from the group consisting: ACTA2, ACVR1, ADM, ALCAM,ANGPT 1, ANGPT 2, ANGPT 4, BAX, BCL2, BCL2L, 18S, 18S, CAV2, CCL1,CCL11, CCL17, CCL19, CCL 2, CCL 20, CCL22, CCL25, CCL27, CCL28, CCL3,COL3A1, COL4A1, COL4A3, CSSF1, CSF2 CSF3, CTGF, CX3CL1, CXCL1, CXCL10,CXCL11, CXCL12, FGF10, FGF11, FGF12, FGF13, FGF17, FGF2, FGF3, FGF5,FGF7, FGF8, FGF9, FIGF, IFNG, IGF1, IGF2, IGFBP1, IGFBP2, IGFBP3,IGFBP3, IGFBP4, IGFBP5, IGFBP6, IGFBP7, IL10, IL11, IL6, IL7, IL8, IL9,ITGA5, ITGAL, ITGAM, ITGB2, KDR, KITLG, LBP, LTA, MMP7, MMP8, MMP9, MPO,NCAM2, NFKB1, NFKB2, NOS2A, OSMR, PDGFA, PDGFB, PECAM1, SMAD6, SMAD7,SOCS1, SOCS3, SOCS5, STAT3, TEK, TGFA, TGFB1, TGFB2, TGFB3, TGFBR1,DCL2L2, BMP1, BMP15, BMP5, BMP3, BMP4, BMP5, BMP6, BMP7, BMP8A, BMP8B,CALCA, CALCB, CAV1, CCL4, CCL4L1, CCL4L2, CCL5, CCL7, CD14, CD4, CD40,CD40LG, CD83, CD8A, CD8B, COL18A1, COL1A1, CXCL13, CXCL2, CXCL5, CXCL9,ECGF1, EDN1, EGF, EGR1, EPO, FADD, FAS, FGF1, FLT1, FN1, GAPDH, GDF3,GDF5, MSTN, GDF9, HGF, HMGB1, IAPP, ICAM2, IFNB1, IL12A, IL13, ILLS,IL16, IL17A, IL18, IL1A, 1L1B, 1L2, IL3, IL4, IL5, MAPK14, MET, MMP1,MMP10, MMP11, MMP12, MMP13, MMP14, MMP15, MMP2, MMP24, MMP3, PF4,PLA2G4A, PTGS1, PTGS2, SELE, SELP, SERPINE1, SLPI, SMAD1, SMAD2, SMAD3,SMAD4, TIE1, TIMP1, TIMP2, TIMP3, TNC, TNF, TNFSF10, VCAM1, VEGFB,VEGFC, XCL1, and XCL2.